Multi-Hop Clustering and Routing Protocol Based on Enhanced Snake Optimizer and Golden Jackal Optimization in WSNs.

A collection of smaller, less expensive sensor nodes called wireless sensor networks (WSNs) use their sensing range to gather environmental data. Data are sent in a multi-hop manner from the sensing node to the base station (BS). The bulk of these sensor nodes run on batteries, which makes replacement and maintenance somewhat difficult. Preserving the network's energy efficiency is essential to its longevity. In this study, we propose an energy-efficient multi-hop routing protocol called ESO-GJO, which combines the enhanced Snake Optimizer (SO) and Golden Jackal Optimization (GJO). The ESO-GJO method first applies the traditional SO algorithm and then integrates the Brownian motion function in the exploitation stage. The process then integrates multiple parameters, including the energy consumption of the cluster head (CH), node degree of CH, and distance between node and BS to create a fitness function that is used to choose a group of appropriate CHs. Lastly, a multi-hop routing path between CH and BS is created using the GJO optimization technique. According to simulation results, the suggested scheme outperforms LSA, LEACH-IACA, and LEACH-ANT in terms of lowering network energy consumption and extending network lifetime.

Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net): Polarization Image Demosaicing Based on Stokes Vector Completion in Low-Light Environment.

Polarization imaging has achieved a wide range of applications in military and civilian fields such as camouflage detection and autonomous driving. However, when the imaging environment involves a low-light condition, the number of photons is low and the photon transmittance of the conventional Division-of-Focal-Plane (DoFP) structure is small. Therefore, the traditional demosaicing methods are often used to deal with the serious noise and distortion generated by polarization demosaicing in low-light environment. Based on the aforementioned issues, this paper proposes a model called Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net) for simulating a sparse polarization sensor acquisition of polarization images in low-light environments. The model consists of two parts: an intensity image enhancement network and a Stokes vector complementation network. In this work, the intensity image enhancement network is used to enhance low-light images and obtain high-quality RGB images, while the Stokes vector is used to complement the network. We discard the traditional idea of polarization intensity image interpolation and instead design a polarization demosaicing method with Stokes vector complementation. By using the enhanced intensity image as a guide, the completion of the Stokes vector is achieved. In addition, to train our network, we collected a dataset of paired color polarization images that includes both low-light and regular-light conditions. A comparison with state-of-the-art methods on both self-constructed and publicly available datasets reveals that our model outperforms traditional low-light image enhancement demosaicing methods in both qualitative and quantitative experiments.

Anti-oxidant effects of herbal residue from Shengxuebao mixture on heat-stressed New Zealand rabbits.

Heat stress can lead to hormonal imbalances, weakened immune system, increased metabolic pressure on the liver, and ultimately higher animal mortality rates. This not only seriously impairs the welfare status of animals, but also causes significant economic losses to the livestock industry. Due to its rich residual bioactive components and good safety characteristics, traditional Chinese medicine (TCM) residue is expected to become a high-quality feed additive with anti-oxidative stress alleviating function. This study focuses on the potential of Shengxuebao mixture herbal residue (SXBR) as an anti-heat stress feed additive. Through the UPLC (ultra performance liquid chromatography) technology, the average residue rate of main active ingredients from SXBR were found to be 25.39%. SXBR were then added into the basal diet of heat stressed New Zealand rabbits at the rates of 5% (SXBRl), 10% (SXBRm) and 20% (SXBRh). Heat stress significantly decreased the weight gain, as well as increased neck and ear temperature, drip loss in meat, inflammation and oxidative stress. Also, the hormone levels were disrupted, with a significant increase in serum levels of CA, COR and INS. After the consumption of SXBR in the basal diet for 3 weeks, the weight of New Zealand rabbits increased significantly, and the SXBRh group restored the redness value of the meat to a similar level as the control group. Furthermore, the serum levels T3 thyroid hormone in the SXBRh group and T4 thyroid hormone in the SXBRm group increased significantly, the SXBRh group showed a significant restoration in inflammation markers (IL-1ß, IL-6, and TNF-α) and oxidative stress markers (total antioxidant capacity, HSP-70, MDA, and ROS) levels. Moreover, the real-time fluorescence quantitative PCR analysis found that, the expression levels of antioxidant genes such as Nrf2, HO-1, NQO1, and GPX1 were significantly upregulated in the SXBRh group, and the expression level of the Keap1 gene was significantly downregulated. Additionally, the SXBRm group showed significant upregulation in the expression levels of HO-1 and NQO1 genes. Western blot experiments further confirmed the up-regulation of Nrf2, Ho-1 and NQO1 proteins. This study provides a strategy for the utilization of SXBR and is of great significance for the green recycling of the TCM residues, improving the development of animal husbandry and animal welfare.

[Methods for research on structures and functions of traditional Chinese medicine proteins].

Traditional Chinese medicine proteins(TCMPs) not only have nutritional values and biological activities but also serve as key enzymes in the synthesis of pharmacodynamic components in traditional Chinese medicines. They play a role in the synthesis of pharmacodynamic components by regulating biosynthesis and selective synthesis pathways and controlling drug quality and stability. The recent years have witnessed great progress in the research on the structures and functions of proteins using various methods and technologies. However, the research on the structures and functions of TCMPs lags behind. Therefore, it is urgent to study the structures and functions of TCMPs using modern means to promote the discovery of innovative drugs based on TCMPs and clarify the synthesis pathways of pharmacodynamic components. This study introduces the latest techniques for studying protein structures and functions, including spectroscopy, mass spectrometry, nuclear magnetic resonance, X-ray crystal diffraction, microscopy, and structure prediction. Furthermore, this paper introduces the methods for protein functional studies, including liquid chromatography-mass spectrometry, co-immunoprecipitation, yeast two-hybrid, and pull-down assay. By systematically reviewing these techniques and methods, this paper provides technical references for the structural identification and functional studies of TCMPs, with the aim of promoting the in-depth exploration of the structures and functions of TCMPs.

[Application strategy of artificial intelligence technology in feed-based development of medicinal and edible homologous traditional Chinese medicine(TCM)resources in era of the "antibiotic ban"].

In the context of the "antibiotic ban" era, the feed conversion of medicinal and edible traditional Chinese medicine(TCM) resources is a research hotspot in the field of antibiotic alternatives development. How to develop feed products that are beneficial to agriculture and livestock while ensuring nutrient balance and precision using medicinal and edible TCM resources as raw materials has become a challenge. Artificial intelligence(AI) technology has unique advantages in feed production and improving the efficiency of intelligent breeding. If AI technology is applied to the feed development of medicinal and edible TCM resources, it is possible to realize feeding and antibiotic-replacement value while ensuring precise nutrition. In order to better apply AI technology in the field of feed development of medicinal and edible TCM resources, this article used CiteSpace software to carry out literature visualization analysis and found that AI technology had a good application in the field of feed formulation optimization in recent years. However, there is still a gap in the research on the intelligent utilization of medicinal and edible TCM resources. Nonetheless, it is feasible for AI technology to be applied to the feed conversion of medicinal and edible TCM resources. Therefore, this article proposed for the first time an intelligent formulation system framework for feed materials derived from medicinal and edible TCM resources to provide new ideas for research in the field of feed development of medicinal and edible TCM resources and the research on the development of antibiotic alternatives. At the same time, it can pave the way for a new green industry chain for contemporary animal husbandry and the TCM industry.

[Comparison of in vivo pharmacokinetics of twelve constituents in Qihe Fenqing Yin in normal rats and diabetic rats].

This paper aims to study the pharmacokinetic differences of twelve effective constituents(succinic acid, neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, protocatechuic aldehyde, caffeic acid, 5-O-ferulogeninic acid, p-coumaric acid, nuciferine, quercetin, oleanolic acid, and ursolic acid) in Qihe Fenqing Yin in normal and diabetic rats. The diabetic rat model was established by a high-fat diet combined with intraperitoneal injection of streptozocin. A UHPLC-QTRAP-MS/MS method was established for the simultaneous determination of 12 constituents in the plasma of normal rats and model rats after a single intragastric administration of Qihe Fenqing Yin. The results show that the established analytical method has a good linear relationship with the 12 components, and the specificity, accuracy, precision, and stability meet the requirements. The computational pharmacokinetic parameters are fitted by DAS 3.2.8 software, and the results show that the half-life time(t_(1/2)) of the other nine components in the model group was longer than that in the normal group except for caffeic acid, 5-O-ferulogeninic acid, and oleanolic acid. The area under curve(AUC_(0-t)) of cryptochlorogenic acid, p-coumaric acid, ursolic acid, and oleanolic acid increases compared with the normal group. Meanwhile, mean residence time(MRT) delays. The "double peaks" of quercetin and nuciferine in the normal group are not observed in the model group, suggesting that the pharmacokinetic parameters of the drugs in the disease state are significantly different.

[Development of green pesticides from traditional Chinese medicines based on artificial intelligence and compatibility of traditional Chinese medicines].

The food security of China as a big agricultural country is attracting increasing attention. With the progress in the traditional Chinese medicine industry, Chinese medicinal materials and their preparations have been gradually developed as agents for disease prevention and with antimicrobial and insecticidal functions in agriculture. Promoting pesticide innovation by interdisciplinary integration has become the trend in pesticide research globally. Considering the increasingly important roles of green pesticides from traditional Chinese medicines and artificial intelligence in pest target prediction, this paper proposed an innovative green control strategy in line with the concepts of ecological sustainable development and food security protection. CiteSpace was used for visual analysis of the publications. The results showed that artificial intelligence had been extensively applied in the pesticide field in recent years. This paper explores the application and development of biopesticides for the first time, with focus on the plant-derived pesticides. The thought of traditional Chinese medicine compatibility can be employed to creat a new promosing field: pesticides from traditional Chinese medicine. Moreover, artificial intelligence can be employed to build the formulation system of pesticides from traditional Chinese medicines and the target prediction system of diseases and pests. This study provides new ideas for the future development and market application of biopesticides, aiming to provide more healthy and safe agricultural products for human beings, promote the innovation and development of green pesticides in China, and protect the sustainable development of the environment and ecosystem. This may be the research hotspot and competition point for the green development of the pesticide industry chain in the future.

[Identification of horn-derived animal medicines based on detection of keratin-derived characteristic peptides].

Due to the highly stable structure of keratin, the extraction and dissolution steps of animal medicines rich in keratin are complex, which seriously restricts the detection efficiency and flux. Therefore, this study simplified the pre-treatment steps of horn samples and optimized the detection methods of characteristic peptides to improve the efficiency of identifying the specificity of horn-derived animal medicines. For detection of the characteristic peptides in horn-derived animal medicines treated with/without iodoace-tamide(IAA), the ion pair conditions of the characteristic peptides were optimized, and the retention time, intensity and other data of the specific peptides were compared between the samples treated with/without IAA. Two pre-treatment methods, direct enzymatic hydrolysis and total protein extraction followed by enzymatic hydrolysis, were used to prepare horn-derived animal medicine samples. The effects of different methods on the detection of specific peptides in the samples of Saiga antelope horn, water buffalo horn, goat horn, and yak horn were compared regarding the retention time of specific peptides and ion intensity. The results indicated that after direct enzymatic hydrolysis, the specific peptides in the samples without IAA treatment can be detected. Compared with the characteristic peptides in the samples treated with IAA, their retention time shifted back and the mass spectrometry response slightly decreased. The specific peptides of the samples without IAA treatment had good specificity and did not affect the specificity identification of horn-derived animal medicines. Overall, the process of direct enzymatic hydrolysis can be used to treat horn samples, omitting the steps of protein extraction and dithiothreitol and IAA treatment, significantly improving the pre-treatment efficiency without affecting the specificity identification of horn-derived animal medicines. This study provides ideas for quality research and standard improvement of horn-derived animal medicines.

Maneuvering the Peroxidase-Like Activity of Palladium-Based Nanozymes by Alloying with Oxophilic Bismuth for Biosensing.

Engineering the catalytic performance of nanozymes is of vital importance for their broad applications in biological analysis, cancer treatment, and environmental management. Herein, a strategy to boost the peroxidase-like activity of Pd-based nanozymes with oxophilic metallic bismuth (Bi) is demonstrated, which is based on the incorporation of oxophilic Bi in the Pd-based alloy nanocrystals (NCs). To synthesize PdBi alloy NCs, a seed-mediated method is employed with the assistance of underpotential deposition (UPD) of Bi on Pd. The strong interaction of Bi atoms with Pd surfaces favors the formation of alloy structures with controllable shapes and excellent monodispersity. More importantly, the PdBi NCs show excellent peroxidase-like activities compared with pristine Pd NCs. The structure-function correlations for the PdBi nanozymes are elucidated, and an indirect colorimetric method based on cascade reactions to determine alkaline phosphatase (ALP) is established. This method has good linear range, low detection limit, excellent selectivity, and anti-interference. Collectively, this work not only provides new insights for the design of high-efficiency nanozymes, expands the colorimetric sensing platform based on enzyme cascade reactions, but also represents a new example for UPD-directed synthesis of alloy NCs.

Polarization image demosaicing and RGB image enhancement for a color polarization sparse focal plane array.

The color division of focal plane (DoFP) polarization sensor structure mostly uses Bayer filter and polarization filter superimposed on each other, which makes the polarization imaging unsatisfactory in terms of photon transmission rate and information fidelity. In order to obtain high-resolution polarization images and high-quality RGB images simultaneously, we simulate a sparse division of focal plane polarization sensor structure, and seek a sweet spot of the simultaneous distribution of the Bayer filter and the polarization filters to obtain both high-resolution polarization images and high-quality RGB images. In addition, From the perspective of sparse polarization sensor imaging, leaving aside the traditional idea of polarization intensity interpolation, we propose a new sparse Stokes vector completion method, in which the network structure avoids the introduction and amplification of noise during polarization information acquisition by mapping the S1 and S2 components directly. The sparsely polarimetric image demosaicing (Sparse-PDM) model is a progressive combined structure of RGB image artifact removal enhancement network and sparsely polarimetric image completion network, which aims to compensate sparsely polarimetric Stokes parameter images with the de-artifacts RGB image as a guide, thus achieving high-quality polarization information and RGB image acquisition. Qualitative and quantitative experimental results on both self-constructed and publicly available datasets prove the superiority of our method over state-of-the-art methods.

End-to-end neural network for pBRDF estimation of object to reconstruct polarimetric reflectance.

Estimating the polarization properties of objects from polarization images is still an important but extremely undefined problem. Currently, there are two types of methods to probe the polarization properties of complex materials: one is about the equipment acquisition, which makes the collection of polarization information unsatisfactory due to the cumbersome equipment and intensive sampling, and the other is to use polarized imaging model for probing. Therefore, the accuracy of the polarized imaging model will be crucial. From an imaging perspective, we propose an end-to-end learning method that can predict accurate, physically based model parameters of polarimetric BRDF from a limited number of captured photographs of the object. In this work, we first design a novel pBRDF model as a powerful prior knowledge. This hybrid pBRDF model completely defines specular reflection, body scattering and directional diffuse reflection in imaging. Then, an end-to-end inverse rendering is performed to connect the multi-view measurements of the object with the geometry and pBRDF parameter estimation, and a reflectance gradient consistency loss is introduced to iteratively estimate the per-pixel normal, roughness, and polarimetric reflectance. Real-world measurement and rendering experiments show that the results obtained by applying our method are in strong agreement with ground truth, validating that we can reproduce the polarization properties of real-world objects using the estimated polarimetric reflectance.

Texture characterization and classification of polarized images based on multi-angle orthogonal difference.

The Local Binary Pattern (LBP) and its variants are capable of extracting image texture and have been successfully applied to classification. However, LBP has not been used to extract and describe the texture of polarized images, and simple LBP cannot characterize the polarized texture information from different polarizations of angles. In order to solve these problems, we propose a new multi-angle orthogonal difference polarization image texture descriptor (MODP_ITD) by analyzing the relationship between the difference of orthogonal difference polarization images from different angles and the pixel intensity distribution in the local neighborhood of images from different angles. The MODP_ITD consists of three patterns: multi-angle polarization orthogonal difference local binary pattern (MODP_LBP), multi-angle polarization orthogonal difference local sampling point principal component sequence pattern (MODP_LPCSP) and multi-angle orthogonal difference polarization local difference binary pattern (MODP_LDBP). The MODP_LBP extracts local corresponding texture characteristics of polarized orthogonal difference images from multiple angles. The MODP_LPCSP sorts the principal component order of each angle orthogonal difference local sampling point. The MODP LDBP extracts the local difference characteristics between different angles by constructing a new polarized image. Then, the frequency histograms of MODP_LBP, MOD_LPCSP ,and MODP_LDBP are cascaded to generate MODP_ITD, so as to distinguish local neighborhoods. By using vertical and parallel polarization and unpolarized light active illumination, combined with the measurements at three different detection zenith angles, we constructed a polarization texture image database. A substantial number of experimental results on the self-built database show that our proposed MODP_ITD can represent the detailed information of polarization images texture. In addition, compared with the existing LBP methods, The MODP_ITD has a competitive advantage in classification accuracy.

Discovery of Elaphuri Davidiani Cornu-specific peptide biomarkers by peptidomics analysis-based method.

Elaphuri Davidiani Cornu (EDC) is the antler of the male Père David's deer, which has been reported to have multiple biological activities, and its use as a traditional Chinese medicine (TCM) in China has been known for thousands of years. However, EDC is difficult to distinguish from other related species-derived antlers in powder or extract form in TCM clinic use, such as Cervus elaphus Cornu (CEC) and Cervus nippon Cornu (CNC), both derived from Cervidae and easily confused with EDC. In this study, a strategy using peptidomics combined with mathematics set analysis was used to identify EDC-specific peptide biomarkers, and four specific peptide biomarkers (Pep-E1-E4) were identified and validated. Pep-E1, Pep-E3, and Pep-E4 could be exclusively detected in EDC samples, with relative peak areas of 0.298 ± 0.060, 0.039 ± 0.015, and 0.037 ± 0.008, whereas Pep-E2 showed relative peak area of 0.516 ± 0.101 in EDC, 0.132 ± 0.026 in CEC, and 0.136 ± 0.047 in CNC samples, respectively. These four peptides are applicable to distinguish EDC from CEC and CNC, which is of great significance for the quality control of EDC.

Novel and efficient techniques in the discovery of antioxidant peptides.

As a research hotspot in food science and nutrition, antioxidant peptides can function by scavenging free radicals, inhibiting peroxides, and chelating metal ions. Therefore, how to efficiently discover and screen antioxidant peptides has become a key issue in research and production. Traditional discovery methods are time-consuming and costly, but also challenging to resolve the quantitative structure-activity relationship of antioxidant peptides. Several novel techniques, including artificial intelligence, molecular docking, bioinformatics, quantum chemistry, phage display, switchSENSE, surface plasmon resonance, and fluorescence polarization, are emerging rapidly as solutions. These techniques possess efficient capability for the discovery of antioxidant peptides, even with the potential for high-throughput screening. In addition, the quantitative structure-activity relationship can be resolved. Notably, combining these novel techniques can overcome the drawbacks of a single one, thus improving efficiency and expanding the discovery horizon. This review has summarized eight novel and efficient techniques for discovering antioxidant peptides and the combination of techniques. This review aims to provide scientific evidence and perspectives for antioxidant peptide research.

Micro(nano)plastics in commercial foods: A review of their characterization and potential hazards to human health.

Micro (nano)plastics (MNPs) are pollutants of worldwide concern for their ubiquitous environmental presence and associated impacts. The higher consumption of MNPs contaminated commercial food can cause potential adverse human health effects. This review highlights the evidence of MNPs in commercial food items and summarizes different sampling, extraction, and digestion techniques for the isolation of MNPs, such as oxidizing digestion, enzymatic digestion, alkaline digestion and acidic digestion. Various methods for the characterization and quantification of microplastics (MPs) are also compared, including µ-Raman spectroscopy, µ-Fourier transform infrared spectroscopy (FTIR), thermal analysis and Scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX). Finally, we share our concerns about the risks of MNPs to human health through the consumption of commercial seafood. The knowledge of the potential human health impacts at a subcellular or molecular level of consuming mariculture products contaminated with MNPs is still limited. Moreover, MNPs are somewhat limited, hard to measure, and still contentious. Due to the nutritional significance of fish consumption, the risk of exposure to MNPs and the associated health effects are of the utmost importance.

Combination of mathematics and label-free proteomics for discovering keratin-derived specific peptide biomarkers to distinguish animal horn-derived traditional Chinese medicines.

Saiga antelope horn and Rhinoceros horn have been used in traditional Chinese medicine for thousands of years. However, due to the protection of wildlife, the application of these rare animal horns has been restricted or prohibited. Therefore, water buffalo horn, goat horn, and yak horn have been applied as alternatives to Rhinoceros horn or Saiga antelope horn in a clinic. It is extremely difficult to distinguish normal animal horns in powdered or decocted form, especially identifying related species such as water buffalo horn, yak horn, and cattle horn. In this work, mathematics set and label-free proteomics analysis were combined for discovering keratin-derived specific peptide biomarkers. By using mathematics set analysis after nano liquid chromatography-tandem mass spectrometry-based proteomics, the selected species-specific peptides could be used to identify the authenticity of the Saiga antelope horn and goat horn. Furthermore, peptide biomarkers were selected to distinguish related species-derived horns, water buffalo horn, yak horn, and cattle horn. In total, eight peptide biomarkers were selected and applied for simultaneously distinguishing different horn samples. The present strategy provides a method for peptide biomarkers discovery and also has positive significance for ensuring the quality and efficacy of animal horn-derived traditional Chinese medicines and their products.

Polarized light transmission characteristics in a smoky ellipsoidal particle medium.

True natural environments are more complex, and light travels through non-spherical particle media, which can affect the transmission of light. The medium environment of non-spherical particles is more common than that of spherical particles, and some studies have shown that there are differences between spherical and non-spherical particles in polarized light transmission. Therefore, the use of spherical particles instead of non-spherical particles will result in great error. In view of this feature, this paper samples the scattering angle based on the Monte Carlo method, and then constructs a simulation model of a random sampling fitting phase function suitable for ellipsoidal particles. In this study, yeast spheroids and Ganoderma lucidum spores were prepared. The effects of different polarization states and optical thicknesses on the transmission of polarized light at three wavelengths were investigated using ellipsoidal particles with a ratio of 1.5 transverse to vertical axes. The results show that when the concentration of the medium environment increases, the polarized lights of different states all show obvious depolarization, but circularly polarized light has better polarization-preserving characteristics than linearly polarized light, and polarized light with larger wavelengths also shows more stable optical properties. When yeast and Ganoderma lucidum spores were used as the transport medium, the degree of polarization of polarized light had the same trend. However, the equal volume radius of yeast particles is smaller than that of Ganoderma lucidum spores, so when the laser is in the yeast particle medium, the polarization-maintaining property of polarized light is superior. This study provides an effective reference for the variation of polarized light transmission in an atmospheric transmission environment with heavy smoke.

Analysis of the effect of optical thickness on polarization in a sea fog stratified environment.

Since there are usually multiple layers present in a real-world sea fog environment, and because previous studies have tended to analyze sea fog as a single layer rather than as refined layered sea fog, this paper splits sea fog into two categories: water fog and salt fog double-layer environments. By adjusting the optical thickness of the two layers of media, we may investigate the issue of the law governing the transmission of polarized light. In this paper, the analysis is mainly carried out through a simulation and experimental tests. The simulation portion is based mostly on the improved layered Monte Carlo approach, which builds a simulation model more appropriate for multilayer non-spherical media by using the accumulation principle to determine the scattering and transmission properties between layers. The tests are conducted by altering the double-layer medium's optical thickness, incoming wavelength, and polarization state, and then getting the polarization information of visible light after transmission through the complicated environment. The findings demonstrate that the optical thickness of the sea fog double-layer media affects polarized light transmission in a non-negligible way. Longer wavelength polarized light may keep polarization information better as the optical thickness increases, and circularly polarized light has polarization-preserving properties that are superior to linearly polarized light. By contrasting the simulation findings with the experimental data, the consistency of the two conclusions is confirmed, and the study offers a helpful resource for the transmission of polarized light in the sea fog environment.

Organochlorine pesticides (OCPs) in freshwater resources of Pakistan: A review on occurrence, spatial distribution and associated human health and ecological risk assessment.

The extensive use of organochlorine pesticides (OCPs) has resulted in the widespread contamination of different environmental matrices in Pakistan. Freshwater bodies are also prone to OCPs contamination as they receive agricultural and industrial runoff from different sources. In the present study, the data regarding OCPs' fate and distribution in freshwater resources of Pakistan was reviewed and associated risks to human and ecological health were assessed. Among all the OCPs, DDTs were more prevalent with the highest mean concentration of 2290 ng/L observed in River Ravi (Lahore and Sahiwal District). Human health risk assessment showed a higher risk to the children with high Hazard Quotient (HQ) values ranging between 4.1 × 10-9- 295 for Aldrin. The River Ravi (Lahore and Sahiwal District), the River Sutlej (Kasur & Bahawalpur District), and the River Kabul (Nowshehra District) were categorized as high-risk water bodies based on Hazard Index (HI) and Non-Cancer Risk (CRI) index values > 10. Ecological risk assessment revealed a higher risk posed to invertebrate species from DDT exposure. In summary, this review highlights the occurrence and distribution of OCPs and their associated human health and ecological risks in freshwater bodies of Pakistan and also contributes to signifying the need for proper management and regulation of banned pesticides and future research perspectives.

CenterPNets: A Multi-Task Shared Network for Traffic Perception.

The importance of panoramic traffic perception tasks in autonomous driving is increasing, so shared networks with high accuracy are becoming increasingly important. In this paper, we propose a multi-task shared sensing network, called CenterPNets, that can perform the three major detection tasks of target detection, driving area segmentation, and lane detection in traffic sensing in one go and propose several key optimizations to improve the overall detection performance. First, this paper proposes an efficient detection head and segmentation head based on a shared path aggregation network to improve the overall reuse rate of CenterPNets and an efficient multi-task joint training loss function to optimize the model. Secondly, the detection head branch uses an anchor-free frame mechanism to automatically regress target location information to improve the inference speed of the model. Finally, the split-head branch fuses deep multi-scale features with shallow fine-grained features, ensuring that the extracted features are rich in detail. CenterPNets achieves an average detection accuracy of 75.8% on the publicly available large-scale Berkeley DeepDrive dataset, with an intersection ratio of 92.8% and 32.1% for driveableareas and lane areas, respectively. Therefore, CenterPNets is a precise and effective solution to the multi-tasking detection issue.