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With the increase in global aquaculture production, managing waste from aquatic biomass has become a significant concern. This research aimed to develop a sustainable valorization approach for recovering calcium-rich fish, including mackerel tuna and pangas bone and shrimp shell powders. The powders were characterized by various physicochemical and nutritional parameters, including proximate composition, amino acids, protein solubility, water holding capacity (WHC), oil holding capacity (OHC), and heavy metal contents. Color analysis and structural examination were carried out using field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDX), and Fourier-transform infrared spectroscopy (FT-IR), and in vitro radical scavenging activity was assessed. Significant protein content was observed in the powders, which was highest in shrimp shell powder (SSP) at 37.78%, followed by 32.29% in pangas bone powder (PBP) and 30.28% in tuna bone powder (TBP). The ash content was consistent in PBP and TBP at around 62.80%, while SSP had a lower ash content of 36.58%. Amino acid analysis detected 14 different amino acids in the recovered powders. Notably, SSP demonstrated the highest WHC and OHC values (2.90 and 2.81, respectively), whereas TBP exhibited the lowest values (1.11 for WHC and 1.21 for OHC). FE-SEM revealed the compact structure of TBP and PBP, contrasting with the porous surface of SSP. EDX analysis indicated higher calcium (24.52%) and phosphorus (13.85%) contents in TBP, while SSP was enriched in carbon (54.54%). All detected heavy metal concentrations were within acceptable limits. The recovered powders demonstrated significant ABTS free radical scavenging activity. The findings of this study suggest the suitability of the recovered powders for various food and pharmaceutical applications. [ABSTRACT FROM AUTHOR]

The rapid and unprecedented expansion of the global population highlights concerns about the sufficiency of food resources to sustain this growth. This study investigates and substantiates the feasibility of renewable food resources in order to meet the nutritional requirements of consumers. Three edible aquatic weeds, helencha (Enhydra fluctuans), malancha (Alternanthera philoxeroides), and kalmi (Ipomoea aquatica), were used to produce edible paper sheets. The composition of the raw aquatic weeds and paper sheet samples was analyzed, including the proximate composition, amino acid content, minerals and heavy metal contents, and bioactive compounds. The dried raw aquatic weeds and paper sheets showed similar proximate compositions, with carbohydrates being the highest component (50.38–64.63%), followed by crude protein (15.25–19.13%), ash (9.30–15.88%), and lipid (1.55–3.43%). The raw weeds and paper sheets were rich in essential minerals like Na, Ca, and Zn with contents ranging from 27.7 mg/100 g to 30.4 mg/100 g, 126.8 mg/100 g to 489.65 mg/100 g, and 4.5 mg/100 g to 16.3 mg/100 g, respectively. Acceptable levels of heavy metals, including Ni, Pb, and Cu, were found. The paper sheets contained seven essential and eight non-essential amino acids. Among the essential amino acids, the phenylalanine content was the highest at 2735.9 mg/100 g in E. fluctuans paper sheets, followed by methionine at 2377.29 mg/100 g in the raw E. fluctuans and histidine at 1972.6 mg/100 g in E. fluctuans paper sheets. A. philoxeroides sheets showed the highest total amino acid content (16,146.81 mg/100 g), while I. aquatica showed the lowest (13,118.67 mg/100 g). The aquatic weed paper sheets were rich in bioactive compounds, and the numbers in E. fluctuans, A. philoxeroides, and I. aquatica paper sheets were 31, 33, and 40, respectively. There were no significant changes in the nutritional content of the aquatic weeds in paper sheet form compared with the raw weeds, which suggests promising prospects for their production and consumption as a source of nutrition and bioactive compounds. [ABSTRACT FROM AUTHOR]

The above article from IET Communications, published online on 17 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. [ABSTRACT FROM AUTHOR]

Peoples can readily and affordably obtain small indigenous fish species (SIS), with a maximum length of 25 cm, that can be found in niches in small bodies of water (such as canals, ponds, wetlands, streams, etc.). SIS contribute valuable and significant macro- and micronutrients in Bangladesh. SIS are excellent suppliers of vitamins and minerals. The main micronutrients are copper, iodine, iron, selenium, chromium, and zinc, which can help prevent mineral deficiency and enzymatic response in the human body. SIS, such as the mola (Amblypharyngodon mola), cotio (Osteobrama cotio), darkina (Esomus danricus), etc., are easily digestible by the human gut. Nutrients of these fish are quickly accessible by the intestine. These fish are comparatively cheap and easy to capture and promote the presence of particular vitamins in SIS, including A, D, E, and K, as well as vitamin B components. This review paper focuses on the SIS nutritional contribution and their efficacy of preventing malnutrition in developing countries such as Bangladesh and the whole world. [ABSTRACT FROM AUTHOR]

The Atlantic salmon is an extremely popular fish for its nutritional value and unique taste among several fish species. Researchers are focusing on the utilization of Atlantic salmon waste for generating protein hydrolysates rich in peptides and amino acids and investigating their health benefits. Several technological approaches, including enzymatic, chemical, and the recently developed subcritical water hydrolysis, are currently used for the production of Atlantic salmon waste protein hydrolysates. Hydrolyzing various wastes, e.g. , heads, bones, skin, viscera, and trimmings, possessing antioxidant, blood pressure regulatory, antidiabetic, and anti-inflammatory properties, resulting in applications in human foods and nutraceuticals, animal farming, pharmaceuticals, cell culture, and cosmetics industries. Furthermore, future applications, constraints several challenges associated with industrial hydrolysate production, including sensory, safety, and economic constraints, which could be overcome by suggested techno processing measures. Further studies are recommended for developing large-scale, commercially viable production methods, focusing on eradicating sensory constraints and facilitating large-scale application. [Display omitted] • Atlantic salmon waste represents a unique source for protein hydrolysates. • Subcritical water hydrolysis is a novel approach for hydrolysate production. • Various crucial health benefits are executed by Atlantic salmon waste hydrolysates. • Poses various applications in the food, pharmaceutical, and cosmetics industries. • Mitigation strategies for production are explored for industrial applications. [ABSTRACT FROM AUTHOR]

Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model's efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification. [ABSTRACT FROM AUTHOR]

The design and implementation of energy-efficient routing protocols for next-generation wireless sensor networks is always a challenge due to limited power resource capabilities. Hierarchical (clustering) routing protocols appeared to be a remarkable solution for extending the lifetime of wireless sensor networks, particularly in application-aware (threshold-sensitive) and heterogeneity-aware cluster-based routing protocols. In this article, we propose a protocol, namely, Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol. It is a heterogeneity-aware and threshold-based protocol that provides a better solution to existing problems in next-generation wireless sensor networks. During execution, the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol splits the entire network area into several zones to manage network traffic efficiently. In the first step, Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol is designed for a homogeneous network where the initial energy of all the nodes is the same. Thereafter, we bring in heterogeneity in the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol execution environment to optimize its energy consumption. By investigating the performance of the various numbers of divisions, it is proved that the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol with 9 zonal divisions has higher stability and throughput. The performance of the proposed Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol is compared with those of Stable Election Protocol, Low-Energy Adaptive Clustering Hierarchy, Modified Low-Energy Adaptive Clustering Hierarchy, and Gateway-Based Energy-Efficient Routing Protocol through computer simulations. Simulation results verify the improved performance of the proposed Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol in terms of network stability, lifetime, and throughput. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an irreversible illness of the brain impacting the functional and daily activities of elderly population worldwide. Neuroimaging sensory systems such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) measure the pathological changes in the brain associated with this disorder especially in its early stages. Deep learning (DL) architectures such as Convolutional Neural Networks (CNNs) are successfully used in recognition, classification, segmentation, detection, and other domains for data interpretation. Data augmentation schemes work alongside DL techniques and may impact the final task performance positively or negatively. In this work, we have studied and compared the impact of three data augmentation techniques on the final performances of CNN architectures in the 3D domain for the early diagnosis of AD. We have studied both binary and multiclass classification problems using MRI and PET neuroimaging modalities. We have found the performance of random zoomed in/out augmentation to be the best among all the augmentation methods. It is also observed that combining different augmentation methods may result in deteriorating performances on the classification tasks. Furthermore, we have seen that architecture engineering has less impact on the final classification performance in comparison to the data manipulation schemes. We have also observed that deeper architectures may not provide performance advantages in comparison to their shallower counterparts. We have further observed that these augmentation schemes do not alleviate the class imbalance issue. [ABSTRACT FROM AUTHOR]

This correspondence proposes a jointly-designed quasi-cyclic (QC) low-density parity-check (LDPC)-coded multi-relay cooperation with a destination node realized by multiple receive antennas. First, a deterministic approach is utilized to construct different classes of binary QC-LDPC codes with no length-4 cycles. Existing methods put some limitations in terms of code length and rate in order to provide high error-correction performance. Therefore, this article gives three classes of QC-LDPC codes based on a combinatoric design approach, known as cyclic difference packing (CDP), with flexibility in terms of code-length and rate selection. Second, the proposed CDP-based construction is utilized to jointly-design QC-LDPC codes for coded-relay cooperation. At the receiver, the destination node is realized by multiple receive antennas, where maximal-ratio combining (MRC) and sum-product algorithm (SPA)-based joint iterative decoding are utilized to decode the corrupted sequences coming from the source and relay nodes. Simulation results show that the proposed QC-LDPC coded-relay cooperations outperform their counterparts with a coding gain of about 0.25 dB at bit-error rate (BER) 10 − 6 over a Rayleigh fading channel in the presence of additive white Gaussian noise. Furthermore, the extrinsic-information transfer (EXIT) chart analysis has been used to detect the convergence threshold of proposed jointly-designed QC-LDPC codes. Numerical analysis shows that the proposed jointly-designed QC-LDPC codes provide a better convergence as compared to their counterparts under the same conditions. [ABSTRACT FROM AUTHOR]

An emerging narrowband radio technology named narrowband Internet of Things (NB‐IoT) based on cellular communications has been standardised by the 3rd Generation Partnership Project in release‐13. It is specifically designed to provide low‐power wide‐area connectivity for the IoT. In this study, the authors derive an analytical signal model for Long Term Evolution (LTE)‐based uplink NB‐IoT systems. The problem of channel equalisation for the uplink NB‐IoT systems having mismatched baseband processing and sampling rate between transmitter and receiver is addressed. A relation between the channel impulse response and the channel frequency‐domain equalisation coefficients is derived under imbalanced baseband processing and sampling rate environment. Accordingly, a novel one‐tap frequency‐domain channel equalisation algorithm specific to NB‐IoT uplink systems is proposed based on the resultant channel coefficients. Comprehensive link‐level simulations are carried out to validate the potentiality of the proposed equalisation method, compared with other conventional techniques like zero forcing and minimum mean square error equalisers in terms of symbol error rate and throughput against signal‐to‐noise ratio. Simulation results indicate that the proposed equaliser outperforms the existing linear equalisers for all cases of evaluation. [ABSTRACT FROM AUTHOR]