Comprehensive risk assessment and interactions of fluoride and boron ions in the water-sediments interface: The Red Sea, Egypt.

This study presents a baseline evaluation of the distribution, human and ecotoxicological risk, and the potential interactions of fluoride and boron in the water-sediment interface in 25 locations from incredible Red Sea tourist destinations. Results showed comparable levels of B and F in the water and sediments with previous literature. Significant positive correlation was found between B and F (r = 0.57; P<0.01). Based on the sediment/liquid partition coefficient (Kd), F is more likely to be released from the sediment into seawater (logKd< 3) than B (3< logKd< 4). pH and alkalinity may affect water-sediment interactions of B and F, respectively, while SO42- and Cl- ions had no significant effect on adsorption ability of F and B. The majority of minerals had average saturation Index (SI) > 1 referring to the over saturation of seawater with these minerals and their inability to dissolve. The formation of CF, FAP, and CFAP may be related to the high correlation between Fw (r = 0.928, P< 0.01; r = 0.527, P< 0.01; r = 0.608, P< 0.01) and Bw (r = 0.38, P< 0.05; r = 0.38, P< 0.05; r = 0.397, P< 0.05). Total hazard quotient (THQ) for children and adults were <1, revealing no health risks from exposure to B and F through ingestion and skin contact while swimming. The risk characterization ratio; RCRmix(MEC/PNEC) showed high short-term risks to aquatic organisms. Further investigations might emphasis on emerging mitigation strategies to address these concerns.

Chemical speciation and comprehensive risk assessment of metals in sediments from Nabq protectorate, the Red Sea using individual and synergistic indices.

The study evaluates metal concentrations, distributions, contamination, risk, sources, fractionation, and mobility in Nabq Protectorate sediments, revealing a metal content order of Fe, Mn, Pb, Ni, and Cd. Metals are dominated by residual fractions, with Cd (83.70 %) > Ni (82.98 %) > Pb (80.96 %) > Fe (80.31 %) > Mn (76.65 %) reflecting the natural sources of investigated metals. Mn (23.35 %) was the most abundant mobile metal, and the sediments of the protectorate had low toxicity and moderate risk according to the synergistic indices (1 ≤ mRAC<10 and ERM; 5-10). The results from the proposed individual indices showed that Mn, Fe, and Pb are the most bioavailable (BIM 0.1-0.4), Cd, Mn, Ni, Fe, and Pb are of moderate mobility (MIM 0.1-0.4), and Cd is the most available (ARIM 5-10), with Cd posing the most ecological risk. The total hazard quotient (THQ) for child was greater than one, exposure to manganese through ingestion and skin contact while swimming may endanger human health.

Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model.

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and prevention of eventual blindness. Traditional diagnostic methods, relying on human interpretation of medical images, face challenges in terms of accuracy and efficiency. In the present research, we introduce a novel method that offers superior precision in DR diagnosis, compared to traditional methods, by employing advanced deep learning techniques. Central to this approach is the concept of transfer learning. This entails the utilization of pre-existing, well-established models, specifically InceptionResNetv2 and Inceptionv3, to extract features and fine-tune selected layers to cater to the unique requirements of this specific diagnostic task. Concurrently, we also present a newly devised model, DiaCNN, which is tailored for the classification of eye diseases. To prove the efficacy of the proposed methodology, we leveraged the Ocular Disease Intelligent Recognition (ODIR) dataset, which comprises eight different eye disease categories. The results are promising. The InceptionResNetv2 model, incorporating transfer learning, registered an impressive 97.5% accuracy in both the training and testing phases. Its counterpart, the Inceptionv3 model, achieved an even more commendable 99.7% accuracy during training, and 97.5% during testing. Remarkably, the DiaCNN model showcased unparalleled precision, achieving 100% accuracy in training and 98.3% in testing. These figures represent a significant leap in classification accuracy when juxtaposed with existing state-of-the-art diagnostic methods. Such advancements hold immense promise for the future, emphasizing the potential of our proposed technique to revolutionize the accuracy of DR and other eye disease diagnoses. By facilitating earlier detection and more timely interventions, this approach stands poised to significantly reduce the incidence of blindness associated with DR, thus heralding a new era of improved patient outcomes. Therefore, this work, through its novel approach and stellar results, not only pushes the boundaries of DR diagnostic accuracy but also promises a transformative impact in early detection and intervention, aiming to substantially diminish DR-induced blindness and champion enhanced patient care.

An efficient watermarking algorithm for digital audio data in security applications.

Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor. In this paper, we propose a fast watermarking system for audio signals operating in the hybrid transform domain formed by the fractional Charlier transform (FrCT) and the dual-tree complex wavelet transform (DTCWT). The central idea of the proposed algorithm is to parallelize the intensive and repetitive steps in the audio watermarking system and then implement them simultaneously on the available physical cores on an embedded systems cluster. In order to have a low power consumption and a low-cost cluster with a large number of physical cores, four Raspberry Pis 4B are used where the communication between them is ensured using the Message Passing Interface (MPI). The adopted Raspberry Pi cluster is also characterized by its portability and mobility, which are required in watermarking-based smart city applications. In addition to its resistance to any possible manipulation (intentional or unintentional), high payload capacity, and high imperceptibility, the proposed parallel system presents a temporal improvement of about 70%, 80%, and 90% using 4, 8, and 16 physical cores of the adopted cluster, respectively.

Studies of Performance of Cs2TiI6-XBrX (Where x = 0 to 6)-Based Mixed Halide Perovskite Solar Cell with CdS Electron Transport Layer.

The present research work represents the numerical study of the device performance of a lead-free Cs2TiI6-XBrX-based mixed halide perovskite solar cell (PSC), where x = 1 to 5. The open circuit voltage (VOC) and short circuit current (JSC) in a generic TCO/electron transport layer (ETL)/absorbing layer/hole transfer layer (HTL) structure are the key parameters for analyzing the device performance. The entire simulation was conducted by a SCAPS-1D (solar cell capacitance simulator- one dimensional) simulator. An alternative FTO/CdS/Cs2TiI6-XBrX/CuSCN/Ag solar cell architecture has been used and resulted in an optimized absorbing layer thickness at 0.5 µm thickness for the Cs2TiBr6, Cs2TiI1Br5, Cs2TiI2Br4, Cs2TiI3Br3 and Cs2TiI4Br2 absorbing materials and at 1.0 µm and 0.4 µm thickness for the Cs2TiI5Br1 and Cs2TiI6 absorbing materials. The device temperature was optimized at 40 °C for the Cs2TiBr6, Cs2TiI1Br5 and Cs2TiI2Br4 absorbing layers and at 20 °C for the Cs2TiI3Br3, Cs2TiI4Br2, Cs2TiI5Br1 and Cs2TiI6 absorbing layers. The defect density was optimized at 1010 (cm-3) for all the active layers.

Secure biometric systems based on bio-signals and DNA encryption of optical spectrograms.

Recently, biometrics has become widely used in applications to verify an individual's identity. To address security issues, biometrics presents an intriguing window of opportunity to enhance the usability and security of the Internet of Things (IoT) and other systems. It can be used to secure a variety of newly emerging IoT devices. However, biometric scenarios need more protection against different hacking attempts. Various solutions are introduced to secure biometrics. Cryptosystems, cancelable biometrics, and hybrid systems are efficient solutions for template protection. The new trend in biometric authentication systems is to use bio-signals. In this paper, two proposed authentication systems are introduced based on bio-signals. One of them is unimodal, while the other is multimodal. Protected templates are obtained depending on encryption. The deoxyribonucleic acid (DNA) encryption is implemented on the obtained optical spectrograms of bio-signals. The authentication process relies on the DNA sensitivity to variations in the initial values. In the multimodal system, the singular value decomposition (SVD) algorithm is implemented to merge bio-signals. Different evaluation metrics are used to assess the performance of the proposed systems. Simulation results prove the high accuracy and efficiency of the proposed systems as the equal error rate (EER) value is close to 0 and the area under the receiver operator characteristic curve (AROC) is close to 1. The false accept rate (FAR), false reject rate (FRR), and decidability (D) are also estimated with acceptable results of 1.6 × 10-8, 9.05 × 10-6, and 29.34, respectively. Simulation results indicate the performance stability of the proposed systems in the presence of different levels of noise.

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control.

In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content's early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.

A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support.

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client-server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video.

A Secure Framework toward IoMT-Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services.

The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the "HAM10000" dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model's classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

Deploying Machine Learning Techniques for Human Emotion Detection.

Emotion recognition is one of the trending research fields. It is involved in several applications. Its most interesting applications include robotic vision and interactive robotic communication. Human emotions can be detected using both speech and visual modalities. Facial expressions can be considered as ideal means for detecting the persons' emotions. This paper presents a real-time approach for implementing emotion detection and deploying it in the robotic vision applications. The proposed approach consists of four phases: preprocessing, key point generation, key point selection and angular encoding, and classification. The main idea is to generate key points using MediaPipe face mesh algorithm, which is based on real-time deep learning. In addition, the generated key points are encoded using a sequence of carefully designed mesh generator and angular encoding modules. Furthermore, feature decomposition is performed using Principal Component Analysis (PCA). This phase is deployed to enhance the accuracy of emotion detection. Finally, the decomposed features are enrolled into a Machine Learning (ML) technique that depends on a Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), or Random Forest (RF) classifier. Moreover, we deploy a Multilayer Perceptron (MLP) as an efficient deep neural network technique. The presented techniques are evaluated on different datasets with different evaluation metrics. The simulation results reveal that they achieve a superior performance with a human emotion detection accuracy of 97%, which ensures superiority among the efforts in this field.

Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems.

Deep learning is one of the most promising machine learning techniques that revolutionalized the artificial intelligence field. The known traditional and convolutional neural networks (CNNs) have been utilized in medical pattern recognition applications that depend on deep learning concepts. This is attributed to the importance of anomaly detection (AD) in automatic diagnosis systems. In this paper, the AD is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of medical things (IoMT) systems. Deep learning based on the CNN models is employed for this task with training and testing phases. Each input image passes through a series of convolution layers with different kernel filters. For the classification task, pooling and fully-connected layers are utilized. Computer simulation experiments reveal the success and superiority of the proposed models for automated medical diagnosis in IoMT systems.

Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications.

The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

Bacterial classification with convolutional neural networks based on different data reduction layers.

For high accuracy classification of DNA sequences through Convolutional Neural Networks (CNNs), it is essential to use an efficient sequence representation that can accelerate similarity comparison between DNA sequences. In addition, CNN networks can be improved by avoiding the dimensionality problem associated with multi-layer CNN features. This paper presents a new approach for classification of bacterial DNA sequences based on a custom layer. A CNN is used with Frequency Chaos Game Representation (FCGR) of DNA. The FCGR is adopted as a sequence representation method with a suitable choice of the frequency k-lengthen words occurrence in DNA sequences. The DNA sequence is mapped using FCGR that produces an image of a gene sequence. This sequence displays both local and global patterns. A pre-trained CNN is built for image classification. First, the image is converted to feature maps through convolutional layers. This is sometimes followed by a down-sampling operation that reduces the spatial size of the feature map and removes redundant spatial information using the pooling layers. The Random Projection (RP) with an activation function, which carries data with a decent variety with some randomness, is suggested instead of the pooling layers. The feature reduction is achieved while keeping the high accuracy for classifying bacteria into taxonomic levels. The simulation results show that the proposed CNN based on RP has a trade-off between accuracy score and processing time.

An insight into fractionation, toxicity, mobility and source apportionment of metals in sediments from El Temsah Lake, Suez Canal.

El Temsah Lake is one of the most important wetlands in the Suez Canal area and the major source of fish for the area. In this study, the relative role of sediments' geochemical properties and metals geochemical fractionation in determining Cd, Cr, Fe, Mn, Ni, and Pb mobility and toxicity was especially concerned. The results reflected that the increasing order of contamination for the investigated metals according to individual contamination factor (ICF) was: Cr > Mn > Ni > Pb > Cd > Fe. Risk assessment code (RAC) classification showed that the relative amounts of easily dissolved phases of metals in the sediments followed the order of Ni > Cr > Cd > Pb > Fe > Mn. The toxicity as indicated by toxic unit (TU) due to an individual metal followed a descending order of Ni > Cr > Pb > Cd, indicating that Ni and Cr accounted for the majority of the overall sediment toxicity while, Cd contributed the least to the ΣTU. This work constitutes a good basis for further studies about metal fractionation in El Temsah Lake which might help policy makers to take effective decisions for proper management of the lake.

Metals in a northern Nile Delta lake: water, suspended particulates, sediments, and biota.

Lake Edku is one of the important fishing areas in the Nile Delta. It is exposed to different quantities of serious pollutants in particular metals. To overall appraise the risk and status of metals in the lake, a comprehensive study of total concentrations of cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), and zinc (Zn) in sediments and spatial-temporal variations of these metals in the dissolved and suspended particulates, and some tissues of Tilapias niloticus, was conducted from ten sampling sites during 2016. Results showed that none of the investigated metals exceeded the limits considered as hazardous for aquatic life in water. The highest concentrations of Cd, Cu, Ni, and Zn were observed in suspended particulate matter, which may precipitate on the surface of the sediments. Potential ecological risk analysis of the majority of the investigated metals in the sediment indicated that Lake Edku posed a low ecological risk. The estimated values of all metals in tissues of Tilapia niloticus were below the international permissible limits. Moreover, the potential risk of metals to human via the consumption of Tilapia niloticus was estimated using the weekly intake levels, which was lower than the WHO's safe provisional tolerant weekly intake levels. These results prove the importance of performing measurements of contaminants in various compartments of Lake Ecosystem including sediment, biota, and suspended particulate matter for proper management.

Risk assessment and chemical fractionation of selected elements in surface sediments from Lake Qarun, Egypt using modified BCR technique.

In this study, spatial distribution and chemical fractionation were conducted to appraise the overall classification of Al, B, Cd, Cr, and Pb as well as their ecological risks in Lake Qarun sediments. Also, the other chemical components (CaCO3, TOC, SO42-, SiO32-, PO43-, F, Ca, and Mg) that represented the chemical composition of the sediments were determined. Modified BCR sequential extraction technique was used to evaluate the four (acid soluble, reducible, oxidizable and residual) fractions of the surface sediments. The association of Cd was found the highest with acid-soluble fraction (12.16-77.42%), while, Al (89.92-96.83%), B (42.32-93.93%), Cr (86.35-97.42%) and Pb (31.98-86.26%) were reside in residual phase. The degree of surface sediment contamination was computed by risk assessment code (RAC), individual contamination factor (ICF) and Global contamination factor (GCF). Risk assessment code classification showed that the relative amounts of easily dissolved phase of the investigated elements in the sediments are in the order of: Cd > Pb > B > Cr > Al. As a result, Cd had the highest risk to Lake Qarun. This study is a first approach of chemical fractionation of Al, B, Cd, Cr, and Pb in one of the most ecologically important Protectorates declared in the framework of Law 102 of year 1983. Information of this study constitutes a baseline of elements fractionation in Lake Qarun sediments and should be used as a reference for future studies on the changes of labile and residual elements fractions over time.

Double random phase encoding for cancelable face and iris recognition.

Most modern security systems depend on biometrics. Unfortunately, these systems have suffered from hacking trials. If the biometric databases have been hacked and stolen, the biometrics saved in these databases will be lost forever. Thus, there is a desperate need to develop new cancelable biometric systems. The basic concept of cancelable biometrics is to use another version of the original biometric template created through a one-way transform or an encryption scheme to keep the original biometrics safe and away from utilization in the system. In this paper, the optical double random phase encoding (DRPE) algorithm is utilized for cancelable face and iris recognition systems. In the proposed cancelable face recognition scheme, the scale invariant feature transform is used for feature extraction from the face images. The extracted feature map is encrypted with the DRPE algorithm. The proposed cancelable iris recognition system depends on the utilization of two iris images for the same person and features are extracted from both images. The features extracted from one of the iris images are encrypted with the DRPE algorithm, provided that the second phase mask used in the DRPE is generated from the other iris image features. This trend guarantees some sort of feature fusion between the two iris images into a single cancelable iris code and increases user privacy. Simulation results show good performance of the two proposed cancelable biometric schemes even in the presence of noise, especially with the proposed cancelable face recognition scheme.

Fractionation profile and mobility pattern of metals in sediments from the Mediterranean Coast, Libya.

A five-step sequential extraction technique, following Tessier's protocol, has been applied to determine the chemical association of Cd, Cu, Fe, Pb, and Zn with major sedimentary phases (exchangeable, carbonate, manganese and iron oxides, organic and residual fraction) in surface sediments from 14 stations off the Libyan Mediterranean coast. This study is a first approach of chemical fractionation of these metals in one of the most economically important area of the Libyan coastline in Mediterranean Sea. The total metal content was also determined. The total concentration of metals ranged from 5-10.5 mg/kg for Cd, 9.1-22.7 mg/kg for Cu, 141.8-1056.8 mg/kg for Fe, 18.9-56.9 mg/kg for Pb, and 11.6-30.5 mg/kg for Zn. The results of the partitioning study showed that the residual form was the dominant fraction of the selected metals among most of the studied locations. The degree of surface sediment contamination was computed for risk assessment code (RAC), individual contamination factor (ICF), and Global contamination factor (GCF). Risk assessment code classification showed that the relative amounts of easily dissolved phase of trace metals in the sediments are in the order of Pb>Zn>Cd>Cu>Fe. The results of ICF and GCF showed that Sirt and Abu Kammashand had higher GCF than other sites indicating higher environmental risk. In terms of ICF value, a decrease order in environmental risk by trace metals was Pb>Zn>Cu>Cd>Fe. Therefore, Pb had highest risk to water body.

Metals bioavailability in surface sediments off Nile delta, Egypt: Application of acid leachable metals and sequential extraction techniques.

Surface sediments from Nile Delta coast were analyzed for texture, CaCO3, organic matter, fractionation, and acid leachable metals (Cr, Fe, Mn, Ni, Pb, and Zn). The distribution pattern of acid leachable heavy metals in the sediment follows the sequence: Fe>Mn>Pb>Zn>Ni>Cr. All the acid leachable metals didn't exceed the sediment quality guidelines values (effects range low (ERL) and effects range medium (ERM)) and therefore doesn't represent a danger to marine organisms. The correlation of acid leachable Fe, Ni, and Mn indicates a similarity in the association of metals of similar origin. The negative correlation of sand with acid leachable Cr, Fe, Ni, Pb, and Zn indicates that these elements can be easily released by ion exchange processes due to the electrostatic interaction of trace metals as they are weakly bound and is bioavailable to the liquid phase. The acid leachable Cr, Pb, and Zn indicate their association with the CaCO3, while acid leachable Fe, Mn, and Ni are hardly combined with carbonates. All the contents of acid leachable metals are negatively correlated or uncorrelated with OM, which indicates that the studied heavy metals are hardly combined with OM. The results of the partitioning study showed that the residual form was the dominant fraction of the Cr, Fe, and Ni among most of the studied locations. Among the non-lithogenic fractions, the Fe-Mn oxy-hydroxide is the main scavenger for all metals. In terms of risk assessment code (RAC) value, a decrease order in environmental risk by heavy metals was Pb>Mn>Zn>Ni>Cr>Fe. Although the results of the two techniques were not consistent with each other in terms of predicting the metals bioavailability, a combination of total metal concentrations, acid leachable metals, and sequential extraction analysis is necessary to acquire the comprehensive information on the baseline, anthropogenic input, and bioavailability of heavy metals.