The highway contains several lanes, spacious roadways, and high traffic. Expressways convey more people than regular roadways, which is crucial to the nation's economy. A highway crash will kill many people and destroy property. On the freeway, automobiles drop objects, causing major rear-end collisions. The expressway safety detection system uses video cameras to monitor crucial areas of the highway. However, coverage is limited. This research proposes driving vehicle-based expressway tossing object detection to overcome this issue. Mobile road vehicles detect expressway-throwing items. It identifies and records all traffic occurrences in real-time. Throwing things sends an alert message to the control center. After analysis and validation, the control center alerts relevant driving vehicles and manages incidents quickly. Expressway-thrown object detection systems include video capture, video detection and processing, picture transmission, and control centers. This article discusses the throwing object detection system as a moving target recognition and tracking method. Phase correlation estimates and compensates pseudo-motion. Using standard information from the current frame's prior frames creates an acting backdrop model. The current frame's different historical frames efficiently separate the moving items from the foreground. The moving target's shape and location are refined using the two-step morphological technique. To solve data association, the Kalman filter tracks moving objects using centroid, size, and intensity distribution. SVM classifiers categorize and identify moving targets and track non-vehicle targets (throwing items) based on HOG properties. The experimental findings demonstrate that the suggested technique can reliably recognize and track moving targets and discriminate moving object features to detect thrown items.

Cancer of the skin is now one of the most prevalent forms of the disease among people. As a result, accurate diagnosis of malignant lesions is of utmost significance in treating skin cancer. Dermatoscopic images can be used with computer-aided diagnostic tools, which may include machine learning models, to assist medical professionals in diagnosing skin cancer. In this particular research project, skin lesions were classified using image processing and machine learning strategies. Several distinct mathematical techniques have been implemented in the field of image processing in order to improve image quality. Image segmentation utilizing the watershed approach was conducted after an image preparation step, which included filtering the undesired pixels in the pictures. Following that, the lesioned regions were separated, and texture feature extraction was carried out. In the end, the classification was completed using the SVM algorithm, which stands for support vector machines. When the results acquired from the classifiers were compared, it was seen that the SVM classifier had an accuracy of 94.33%.

Plant-derived ingredients almost contribute to 25% of prescription pharmaceuticals yet only a small percentage of the plants in the world have been assessed for their prospective pharmaceutical use. Due to an increasing demand for chemical diversity in screening programs, seeking therapeutic drugs from natural products, interest particularly in edible plants has grown throughout the world. Botanicals and herbal preparations for medicinal usage contain various types of bioactive compounds. The present work focused on the analytical methodologies, which include the phytochemical screening, extraction, isolation and characterization of bioactive compound from the G diversifolia methanolic leaf extract. One phyto-constituent Caffeic acid was isolated from Girardinia diversifolia. The analysis of bioactive compound present in the G diversifolia extract involving the applications of chromatographic techniques such as HPLC and, TLC as well as non-chromatographic techniques such as MS and Fourier Transform Infra-Red (FTIR).

Photocatalysis is considered to be the most efficient treatment as compared to the other methods and is suitable for highly cost-sensitive and energy-restrictive applications. In this research, first, we synthesized magnetic nanoparticles and reported their applications. We are going to publish it in to related communications (part 1 and part 2). In part 1, we synthesized magnetic nanoparticles of Fe3O4 and the versatile ZSM-5/Fe3O4 magnetic nanocomposite for the photocatalytic treatment of wastewater. This paper specifically reports the varying ratios of ZSM-5 and Fe3O4 in the nanocomposites that are 1:1, 1:2 and 1:0.5 and as the concentration of Fe3O4 varied, the properties of the nanocomposite changed as well. Further, these nanocomposites are characterized by X-ray diffraction (XRD), Fourier-transform infrared (FTIR), and several advanced spectroscopy techniques.

In addition to this, in a second communication (part 2), a comparison study is done between the three nanocomposites to study their magnetic behavior and photocatalytic efficiency to treat wastewater. Since these materials are magnetic in nature, therefore, after photocatalysis the material can be easily removed with the help of external magnets. Our approach provides an efficient and comparable synthesis process having photocatalytic applications in treating wastewater.

Enzyme’s subunits interfaces have remarkable potential in drug design as both target and scaffold for their own inhibitors. We show an evolution-driven strategy for the de novo design of peptide inhibitors targeting interfaces of the E. coli’s FoF1-ATP synthase as a case study. The evolutionary algorithm ROSE is applied to generate diversity-oriented peptide libraries by engineering peptide fragments from ATP synthase interfaces. The resulting peptides are scored with PPI-Detect, a sequence-based predictor of protein-protein interactions. Two selected peptides were confirmed by in vitro inhibition and binding tests. The proposed methodology can be widely applied to design peptides targeting relevant interfaces of enzymatic complexes (https://doi.org/10.1002/1873-3468.13988).

Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. The predictions achieved accuracies that ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894–0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subsequently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for the rapid and accurate identification of THPs (https://doi.org/10.3390/antibiotics11030401).

The habitats and ecosystems of the Mediterranean Sea are of prime strategic interest both ecologically and economically. The Ferruginous duck, a species widely distributed in Africa, Europe and Asia, these numbers have experienced declines and changes in distribution in recent decades. The primary reasons for these declines are mainly due to habitat degradation and loss and hunting for local consumption (Robinson & Hughes 2003).

Our study was carried out on the Ferruginous Duck (Aythya nyroca) in Lake Tonga (El Kala National Park), during the period from September 2020 to August 2021. This duck classified as a near threatened species (IUCN, Red list 2022), a regular breeder in this body of water.

We observed the evolution of the numbers of this species during the wintering season, the maximum of 830 of which was noted during the month of December. This Anatidae also prefers to install its nests on islands of Typha angustifolia with a rate of 64%.

It appears that the date of the beginning of the laying is estimated towards the end of April, the nests contain on average 10 to 12 eggs with a maximum of 23 eggs. The monitoring of biometric parameters exposes us to very variable measurements, i.e., an external diameter of 25.8cm [18-35.5], an internal diameter of 16.46cm [12.5-22], a depth of 9.44cm [4.5-17], an elevation of the nests compared to the water around 11.75cm [4-20] and an average inter-nest distance of 5cm [1.75-22.84].

Optimal solutions to packing problems are of the major importance for industry, companies, and business development nowadays. In the Generalized Bin Packing Problem (GBPP) different size containers can store objects and there is a cost per use. Each object is characterized by its volume and a benefit for being packed. The objects are subdivided into two groups, mandatory and non-mandatory. Mandatory items must be packed regardless their perk, while non-mandatory objects are optional to pack. In GBPP the smallest number of containers must be used, while non-mandatory items must be packed to increase the overall utility.

The arc-flow is an effective pseudo-polynomial formulation for Cutting and Packing problems. Here an edge represents each object, and the size of the object depends on the distance between the departure node and the arrival node. The resulting graph represents all possible combinations of objects and the way they can be located within a container.

In this work, we generate a rule-based digraph that introduces the loss arcs representing the empty space in the containers.

We all know that raising children is challenging, primarily when both parents work. It is hard to give 24 hours in such cases. Therefore, we must create something distinctive to benefit parents. Disease-causing bacteria are more likely to infect newborns. Equipment shortages may make matters worse. A model for a reliable and efficient infant monitoring system may improve neonatal care. It is a creative, safe, and innovative infant cradle. Internet of Things devices like Raspberry Pi, Arduino, Humidity & Temperature sensors, Swing Automation, Cry Detecting Systems, Live Video Surveillance, and Cloud Services enhance smartness and innovation. The Cradle has several sensors/modules to monitor the baby's every move: a Humidity & Temperature Sensing Module to detect bed wetness, A Camera on Top of the Cradle for live video footage, and a Cry Detection Circuit to analyze Cry Patterns to activate the swinging mechanism. Sensor and module data will be stored in the cloud and analyzed regularly. A Health Algorithm is used on these datasets to collect information on natural states, which helps diagnose common illnesses. This procedure protects and cares for newborns effectively. Baby breathing problems are detected using a sensor. This sensor detects a baby's breath. Therefore, this technology improves the internet of things child monitoring for parents.

In diabetic patients delayed wound healing and chronic wounds are major complications that have been a reason for serious concern. As reported in the literature, the underlying causes for such wounds are due to reduction of proliferation and migration of different cells like keratinocytes and fibroblasts. Therefore, there is a high demand to bring a wound dressing patch which could provide the advantages of ideal dressings with gaseous exchange, absorption of wound exudate along with release of incorporated therapeutic at sustained action which could support cell proliferation and migration. Such dressing environment ultimately can assist diabetic wound healing. Due to their excellent biocompatibility, effective cell penetration, high fluorescence and specific adsorption of nucleotides, graphene oxide (GO)-derivatives have been identified in a wide range of biomedical applications. The two main methods for producing GO-based wound dressings are solvent blending method and in-situ polymerization. Several in-vitro and in-vivo study reports indicated that application of graphene oxide and its reduced form could promote wound healing by the enhancement of migration and proliferation of keratinocytes. Simultaneously, GO has also shown its potential to induce angiogenic properties that have an active role in any inflammatory event.