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Abstract Background There is growing evidence that supports the role of breastfeeding in reducing the burden of non-communicable diseases (NCDs). There are considerable gaps in breastfeeding outcomes in mothers with chronic diseases due to a lack of knowledge and support in the postpartum period. Mothers who have NCDs and pregnancy complications are at risk of breastfeeding failure. Aim To compare breastfeeding outcomes in mothers with NCDs with healthy mothers and determine the underlying challenges that lead to poor outcomes. Methods A prospective cohort study was conducted among 150 women (50 with high-risk pregnancies (HRP) and 100 with normal pregnancies (NP)). They were recruited from those attending the immunization and outpatient clinics at Sohag General Hospital. Mothers were recruited at 34 weeks gestation and were followed up at 2 weeks, 6 weeks, and 6 months after delivery. A pretested and validated questionnaire was used to collect detailed epidemiological, personal, health-related status, medications, hospitalizations, reproductive history, current delivery, and previous breastfeeding experiences. On follow-up they were assessed for breastfeeding practices, their health and health and growth of their children, and social support. Results Delivery by cesarean section and postpartum bleeding were commoner among HRP patients. Initiation of breastfeeding in the 1st hour of delivery was significantly lower among women with HRP than those with normal pregnancies (48.0% versus 71.0%, p = 0.006). The most common reason for not initiating breastfeeding among the NP group was insufficient milk (34.5%), while in the HRP group, it was the mother’s illness (80.8%). Skin-to-skin contact with the baby after birth was significantly less practiced in the HRP than in the NP group (38.0% vs 64.0% at p = 0.003). Herbs (such as cumin, caraway, cinnamon, aniseed, and chamomile) were the most common pre-lacteal feeds offered (63.0% in NP vs 42.0% in HRP). Artificial milk was more used in HRP than NP (24.0% vs 4.0%). Breast engorgement was 3 times more common in the HRP compared to the NP group (61.5% vs19.6%). Stopping breastfeeding due to breast problems was 2.5 times higher in the HRP than in the NP group (38.5% vs. 15.2%, p = 0.003). Nipple fissures were twice as common among the NP than among the HRP group ((73.0%) vs. (38.5%), p = 0.026). Exclusive breastfeeding during the period of follow-up was lower in the HRP than in the NP group (40.0% vs 61.0%, p

Abstract The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a deadly illness that is complex to discover in an initial phase. Initial diagnosis can significantly enhance the existence rate. Non-ionizing energy, low cost, and convenience make the US a general non-invasive analytical modality for patients with GB diseases. Automatic recognition of GBC from US imagery is a significant issue that has gained much attention from researchers. Recently, machine learning (ML) techniques dependent on convolutional neural network (CNN) architectures have prepared transformational growth in radiology and medical analysis for illnesses like lung, pancreatic, breast, and melanoma. Deep learning (DL) is a region of artificial intelligence (AI), a functional medical tomography model that can help in the initial analysis of GBC. This manuscript presents an Automated Gall Bladder Cancer Detection using an Artificial Gorilla Troops Optimizer with Transfer Learning (GBCD-AGTOTL) technique on Ultrasound Images. The GBCD-AGTOTL technique examines the US images for the presence of gall bladder cancer using the DL model. In the initial stage, the GBCD-AGTOTL technique preprocesses the US images using a median filtering (MF) approach. The GBCD-AGTOTL technique applies the Inception module for feature extraction, which learns the complex and intrinsic patterns in the pre-processed image. Besides, the AGTO algorithm-based hyperparameter tuning procedure takes place, which optimally picks the hyperparameter values of the Inception technique. Lastly, the bidirectional gated recurrent unit (BiGRU) model helps classify gall bladder cancer. A series of simulation analyses were performed to ensure the performance of the GBCD-AGTOTL technique on the GBC dataset. The experimental outcomes inferred the enhanced abilities of the GBCD-AGTOTL in detecting gall bladder cancer.

Abstract This research introduces a novel hybrid system integrating solar drying, solar distillation, and photovoltaic thermal panels, aimed at drying agricultural products, producing clean drinking water, and conserving energy. The system enhances the drying air temperature using thermal energy storage materials and a solar dish concentrator connected to a hot water storage tank, ensuring continuous operation even after sunset. The solar distiller, equipped with energy storage materials and an air injection system, is integrated with an external condenser to condense water vapor before expulsion, thereby increasing freshwater productivity. The results revealed that the developed system achieved an accumulative freshwater productivity of 87.1 L per day. The rates of water removal from dried potato slices ranged from 0.18 to 0.91 kg/h between 8:00 am and 10:00 pm. Additionally, the system achieved a gain output ratio of 1.38, indicating a significant improvement in energy efficiency. This innovative system offers practical solutions to address energy and freshwater scarcity, especially in remote regions of the Middle East and North Africa (MENA) region, which are characterized by high solar irradiance. The implementation of such hybrid systems could contribute to sustainable development by reducing reliance on conventional energy sources and mitigating environmental pollution.

Abstract A novel chelating adsorbent based on (3-iminodiacetic acid) propyltriethoxysilane graphene oxide (IAT-GO) has been developed, showing exceptional promise for capturing lead. IAT-GO is made by combining a high-surface-area graphene oxide with a specially designed chelating ligand, which can selectively and efficiently remove lead. The synthesis of IAT-GO involves a two-step progression. In the first step, covalent bonds form between graphene oxide and (3-aminopropyl)-triethoxysilane (AT) through hydrolysis, condensation, and epoxide ring opening reactions. In the second step, nucleophilic substitution reactions occur between the primary amines and chloroacetic acid (CAA). A comprehensive suite of characterization techniques, including XPS, UV–Vis, XRD, Raman, FTIR, TEM, and SEM, provides detailed insights into the IAT-GO adsorbent's chemical composition and physical form, elucidating its intricate structure and morphology. Optimizing the experimental conditions for using the adsorbent material to remove Pb(II) ions from contaminated water revealed a maximum adsorption capacity of 124.0 mg/g at pH 5 and 30 min. The IAT-GO displays high selectivity for Pb(II) in a mixture of six metal ions containing 100 ppm of each one. Moreover, the IAT-GO shows 100% removal of Pb(II) for concentrations lower than 50 ppm. The excellent fit of the experimental data with the Langmuir adsorption isotherm and pseudo-second-order kinetic models (R2 > 99%) indicates that Pb(II) ion uptake onto the IAT-GO surface occurs via the monolayer formation of mercury ions. IAT-GO demonstrates exceptional potential as an innovative adsorbent for lead-contaminated water. Nitric acid (0.4 M) effectively regenerates the material, while its reusability remains impressive even after five cycles (> 97% removal efficiency). Therefore, this study highlights the development of a groundbreaking material, IAT-GO, with exceptional potential for remediating lead-contaminated water. Its high efficiency, selectivity, reusability, and cost-effectiveness make it a promising candidate for real-world applications.

In recent years, the Internet of Things has played a dominant role in various real-time problems and given solutions via sensor signals. Monitoring the patient health status of Internet of Medical Things (IoMT) facilitates communication between wearable sensor devices and patients through a wireless network. Heart illness is one of the reasons for the increasing death rate in the world. Diagnosing the disease is done by the fusion of multi-sensor device signals. Much research has been done in predicting the disease and treating it correctly. However, the issues are accuracy, consumption time, and inefficiency. To overcome these issues, this paper proposed an efficient algorithm for fusing the multi-sensor signals from wearable sensor devices, classifying the medical signal data and predicting heart disease using the hybrid technique of kernel random forest with the Black Hole Optimization algorithm (KRF-BHO). This KRF-BHO is used for sensor data fusion, while XG-Boost is used to classify echocardiogram images. Accuracy in the training phase with multi-sensor data fusion data set of proposed work KRF-BHO with XGBoost classifier is 94.12%; in the testing phase, the accuracy rate is 95.89%. Similarly, for the Cleveland Dataset, the proposed work KRF-BHO with XGBoost classifier is 95.78%; in the testing phase, the accuracy rate is 96.21%.

Abstract The study aimed to investigate molecularly the presence of flea-borne viruses in infested small ruminants with fleas. It was carried out in Egypt’s Northern West Coast (NWC) and South Sinai Governorate (SSG). Three specific primers were used targeting genes, ORF103 (for Capripoxvirus and Lumpy skin disease virus), NS3 (for Bluetongue virus), and Rdrp (for Coronavirus), followed by gene sequencing and phylogenetic analyses. The results revealed that 78.94% of sheep and 65.63% of goats were infested in the NWC area, whereas 49.76% of sheep and 77.8% of goats were infested in the SSG region. Sheep were preferable hosts for flea infestations (58.9%) to goats (41.1%) in the two studied areas. Sex and age of the animals had no effects on the infestation rate (p > 0.05). The season and site of infestation on animals were significantly different between the two areas (p

Abstract Background The concept of near open sets is a potent tool that empowers researchers to achieve a more encompassing approximation of rough sets, thereby enhancing the accuracy of measurements. The evolution of rough set theory into various generalized forms, based on topological structures, has emerged as a significant approach in the realm of knowledge discovery within databases. Results This paper’s primary contribution lies in the introduction of a novel category of generalized near open sets, termed “inverse simply open sets,” within the context of the $$\text{j}$$ j -neighborhood space. The paper proposes diverse methods for extending the Pawlak’s rough approximations leading to the definition of new approximations in the $$\text{j}$$ j -neighborhood space. By employing these newly introduced generalizations, we establish fresh connections between two pivotal theories, namely “general topology and rough set theory”. Through a comprehensive investigation, we conduct multiple comparisons between our methodology and classical approaches. Furthermore, we showcase practical applications of these techniques within real-life scenarios, demonstrating their utility in decision-making processes. Conclusions We reduced the data’s ambiguity while increasing its accuracy measure. As a result, we may conclude that the proposed approximations were more precise than earlier techniques and contributed to the elimination of data ambiguity in real-world scenarios requiring accurate decisions.

Abstract Background Buffalo spermatozoa have a distinct membrane structure that makes them more vulnerable to cryopreservation, resulting in lower-quality post-thawed sperm. This decreases the success rate of artificial insemination in buffaloes. Understanding and addressing these specific vulnerabilities are essential for improving reproductive techniques in buffalo populations. The properties of cryopreserved buffalo bull semen were examined in this study regarding the impact of adding autologous platelet-rich plasma (PRP) to OptiXcell® or Tris egg yolk-based extenders. Ten buffalo bulls were used to collect semen. Each bull’s ejaculate was separated into two main equal amounts, each of which was then diluted with either OptiXcell® or Tris egg yolk-based extender, supplemented with various PRP concentrations (5%, 10%, and 15%), and the control (0%), before being cryopreserved according to established protocols. Following equilibration and thawing, the quality and functionality of the sperm were evaluated, along with the antioxidant enzyme activities (GSH and TAC), malondialdehyde (MDA) content, and in vivo fertilization rate of the thawed semen. Results All PRP concentrations in both extenders, particularly 10% PRP, improved the quality and functionality of the sperm in both equilibrated and frozen-thawed semen. Additionally, the antioxidant enzyme activities in both extenders were higher in the PRP-supplemented groups compared to the control group in thawed semen (P

Internet of Things (IoT) security is an umbrella term for the strategies and tools that protect devices connected to the cloud, and the network they use to connect. The IoT connects different objects and devices through the internet to communicate with similarly connected machines or devices. An IoT botnet is a network of infected or cooperated IoT devices that can be remotely organized by cyber attackers for malicious purposes such as spreading malware, stealing data, distributed denial of service (DDoS) attacks, and engaging in other types of cybercrimes. The compromised devices can be included in any device connected to the internet and communicate data with, e.g., cameras, smart home appliances, routers, etc. Millions of devices can include an IoT botnet, making it an attractive tool for cyber attackers to launch attacks. Lately, cyberattack detection using deep learning (DL) includes training neural networks on different datasets to automatically detect patterns indicative of cyber threats, which provides an adaptive and proactive approach to cybersecurity. This study presents an evolutionary algorithm with an ensemble DL-based botnet detection and classification (EAEDL-BDC) approach. The goal of the study is to enhance cybersecurity in the cloud-assisted IoT environment via a botnet detection process. In the EAEDL-BDC technique, the primary stage of data normalization using Z-score normalization is performed. For the feature selection process, the EAEDL-BDC technique uses a binary pendulum search algorithm (BPSA). Moreover, a weighted average ensemble of three models, such as the modified Elman recurrent neural network (MERNN), gated recurrent unit (GRU), and long short-term memory (LSTM), are used. Additionally, the hyperparameter choice of the DL approaches occurs utilizing the reptile search algorithm (RSA). The experimental outcome of the EAEDL-BDC approach can be examined on the N-BaIoT database. The extensive comparison study implied that the EAEDL-BDC technique reaches a superior accuracy value of 99.53% compared to other approaches concerning distinct evaluation metrics.

Remote sensing (RS) scene classification has received significant consideration because of its extensive use by the RS community. Scene classification in satellite images has widespread uses in remote surveillance, environmental observation, remote scene analysis, urban planning, and earth observations. Because of the immense benefits of the land scene classification task, various approaches have been presented recently for automatically classifying land scenes from remote sensing images (RSIs). Several approaches dependent upon convolutional neural networks (CNNs) are presented for classifying brutal RS scenes; however, they could only partially capture the context from RSIs due to the problematic texture, cluttered context, tiny size of objects, and considerable differences in object scale. This article designs a Remote Sensing Scene Classification using Dung Beetle Optimization with Enhanced Deep Learning (RSSC-DBOEDL) approach. The purpose of the RSSC-DBOEDL technique is to categorize different varieties of scenes that exist in the RSI. In the presented RSSC-DBOEDL technique, the enhanced MobileNet model is primarily deployed as a feature extractor. The DBO method could be implemented in this study for hyperparameter tuning of the enhanced MobileNet model. The RSSC-DBOEDL technique uses a multi-head attention-based long short-term memory (MHA-LSTM) technique to classify the scenes in the RSI. The simulation evaluation of the RSSC-DBOEDL approach has been examined under the benchmark RSI datasets. The simulation results of the RSSC-DBOEDL approach exhibited a more excellent accuracy outcome of 98.75 % and 95.07 % under UC Merced and EuroSAT datasets with other existing methods regarding distinct measures.

The exponential progress of image editing software has contributed to a rapid rise in the production of fake images. Consequently, various techniques and approaches have been developed to detect manipulated images. These methods aim to discern between genuine and altered images, effectively combating the proliferation of deceptive visual content. However, additional advancements are necessary to enhance their accuracy and precision. Therefore, this research proposes an image forgery algorithm that integrates error level analysis (ELA) and a convolutional neural network (CNN) to detect the manipulation. The system primarily focuses on detecting copy-move and splicing forgeries in images. The input image is fed to the ELA algorithm to identify regions within the image that have different compression levels. Afterward, the created ELA images are used as input to train the proposed CNN model. The CNN model is constructed from two consecutive convolution layers, followed by one max pooling layer and two dense layers. Two dropout layers are inserted between the layers to improve model generalization. The experiments are applied to the CASIA 2 dataset, and the simulation results show that the proposed algorithm demonstrates remarkable performance metrics, including a training accuracy of 99.05%, testing accuracy of 94.14%, precision of 94.1%, and recall of 94.07%. Notably, it outperforms state-of-the-art techniques in both accuracy and precision.

Smart agricultural techniques employ current information and communication technologies, leveraging artificial intelligence (AI) for effectually managing the crop. Recognizing rice seedlings, which is crucial for harvest estimation, traditionally depends on human supervision but can be expedited and enhanced via computer vision (CV). Unmanned aerial vehicles (UAVs) equipped with high-resolution cameras bestow a swift and precise option for crop condition surveillance, specifically in cloudy states, giving valuable insights into crop management and breeding programs. Therefore, we improved an enhanced tunicate swarm algorithm with deep learning-based rice seedling classification (ETSADL-RSC). The presented ETSADL-RSC technique examined the UAV images to classify them into two classes: Rice seedlings and arable land. Initially, the quality of the pictures could be enhanced by a contrast limited adaptive histogram equalization (CLAHE) approach. Next, the ETSADL-RSC technique used the neural architectural search network (NASNet) method for the feature extraction process and its hyperparameters could be tuned by the ETSA model. For rice seedling classification, the ETSADL-RSC technique used a sparse autoencoder (SAE) model. The experimental outcome study of the ETSADL-RSC system was verified for the UAV Rice Seedling Classification dataset. Wide simulation analysis of the ETSADL-RSC model stated the greater accuracy performance of 97.79% over other DL classifiers.

Abstract Background Combination therapies in cancer treatment have demonstrated synergistic or additive outcomes while also reducing the development of drug resistance compared to monotherapy. This study explores the potential of combining the chemotherapeutic agent Paclitaxel (PTX) with Sulforaphane (SFN), a natural compound primarily found in cruciferous vegetables, to enhance treatment efficacy in prostate cancer. Methods Two prostate cancer cell lines, PC-3 and LNCaP, were treated with varying concentrations of PTX, SFN, and their combination. Cell viability was assessed using the thiazolyl blue tetrazolium bromide (MTT) assay to determine the EC50 values. Western blot analysis was conducted to evaluate the expression of Bax, Bcl2, and Caspase-3 activation proteins in response to individual and combined treatments of PTX and SFN. Fluorescent microscopy was employed to observe morphological changes indicative of apoptotic stress in cell nuclei. Flow cytometry analysis was utilized to assess alterations in cell cycle phases, such as redistribution and arrest. Statistical analyses, including Student’s t-tests and one-way analysis of variance with Tukey’s correction, were performed to determine significant differences between mono- and combination treatments. Results The impact of PTX, SFN, and their combination on cell viability reduction was evaluated in a dose-dependent manner. The combined treatment enhanced PTX’s effects and decreased the EC50 values of both drugs compared to individual treatments. PTX and SFN treatments differentially regulated the expression of Bax and Bcl2 proteins in PC-3 and LNCaP cell lines, favoring apoptosis over cell survival. Our data indicated that combination therapy significantly increased Bax protein expression and the Bax/Bcl2 ratio compared to PTX or SFN alone. Flow cytometry analysis revealed alterations in cell cycle phases, including S-phase arrest and an increased population of apoptotic cells. Notably, the combination treatments did not have a discernible impact on necrotic cells. Signs of apoptotic cell death were confirmed through Caspase-3 cleavage, and morphological changes in cell nuclei were assessed via western blot and fluorescent microscopy. Conclusion This combination therapy of PTX and SFN has the potential to improve prostate cancer treatment by minimizing side effects while maintaining efficacy. Mechanistic investigations revealed that SFN enhances PTX efficacy by promoting apoptosis, activating caspase-3, inducing nuclear morphology changes, modulating the cell cycle, and altering Bax and Bcl2 protein expression. These findings offer valuable insights into the synergistic effects of PTX and SFN, supporting the optimization of combination therapy and providing efficient therapeutic strategies in preclinical research.

Network security is a critical aspect of information technology, targeting to safeguard the confidentiality, integrity, and availability of data transmitted across computer networks. Intrusion Detection Systems (IDS) plays an essential role, serving as vigilant sentinels against illegal access, malicious actions, and potential threats. IDS operates on analysing the network or system activities, analyzing patterns, and detecting anomalies that may specify security breaches. The enhancement of network security via the integration of feature selection and machine learning, particularly in the context of IDS. Feature selection methods enable the identification and prioritization of key data attributes, optimizing the performance of machine learning algorithms by focusing on relevant information. Machine learning algorithms, such as decision trees, support vector machines, or neural networks, leverage the chosen features to dynamically adapt and learn from evolving cyber threats. Therefore, this study develops a new gravitational search algorithm-based feature selection with optimal quantum neural network (GSAFS-OQNN) model for intrusion detection and classification. The proposed GSAFS-OQNN approach lies in the effectual detection of intrusions. To accomplish this, the GSAFS-OQNN method exploits a Z-score normalization approach at the preprocessing step. Furthermore, GSAFS-OQNN technique designs the GSAFS model to derive an optimum subset of features. For intrusion detection, quantum neural network (QNN) is applied. Finally, the sandpiper optimization (SPO) technique is used to finetune the parameters of the QNN model. The experimental analysis of GSAFS-OQNN model is implemented on benchmark IDS datasets. The comprehensive results stated the betterment of GSAFS-OQNN method over recent approaches.

Estrus synchronization is important for improving sheep reproduction. To enhance sheep reproduction efficiency, this study investigated the impact of different durations (7 vs. 14 days) and fluorogestone acetate (FGA) doses in intravaginal sponges on estrus synchronization and early pregnancy detection in Ossimi sheep. Two hundred ewes were evenly divided into two groups, each receiving a full 40 mg or a halved 20 mg FGA sponge for their respective durations. The study aimed to optimize breeding efficiency by examining the effectiveness of these treatments in synchronizing estrous cycles and by evaluating the use of serum levels of pregnancy-associated glycoprotein 1 (PAG1) and progesterone (P4) as markers for early pregnancy identification. Prostaglandin F2α and equine chorionic gonadotropin were administered to enhance the synchronization process. Results highlighted that the 7-day treatment protocol significantly improved estrus, pregnancy, and lambing rates compared to the 14-day protocol. Furthermore, pregnant ewes demonstrated elevated levels of PAG1 and P4, with PAG1 levels particularly higher in ewes with multiple pregnancies. The findings underscore that the shorter duration of FGA treatment is more effective for reproductive management in Ossimi sheep without significantly affecting PAG1 levels based on the dose or duration of FGA. PAG1 also proved to be a reliable marker for early pregnancy detection, offering a promising approach to identifying fetal numbers early in pregnancy. This research suggests optimizing FGA sponge use could be cost-efficient for improving reproductive efficiency and early pregnancy management in sheep.

Biomedical image segmentation is a vital task in the analysis of medical imaging, including the detection and delineation of pathological regions or anatomical structures within medical images. It has played a pivotal role in a variety of medical applications, involving diagnoses, monitoring of diseases, and treatment planning. Conventionally, clinicians or expert radiologists have manually conducted biomedical image segmentation, which is prone to human error, subjective, and time-consuming. With the advancement in computer vision and deep learning (DL) algorithms, automated and semi-automated segmentation techniques have attracted much research interest. DL approaches, particularly convolutional neural networks (CNN), have revolutionized biomedical image segmentation. With this motivation, we developed a novel equilibrium optimization algorithm with a deep learning-based biomedical image segmentation (EOADL-BIS) technique. The purpose of the EOADL-BIS technique is to integrate EOA with the Faster RCNN model for an accurate and efficient biomedical image segmentation process. To accomplish this, the EOADL-BIS technique involves Faster R-CNN architecture with ResNeXt as a backbone network for image segmentation. The region proposal network (RPN) proficiently creates a collection of a set of region proposals, which are then fed into the ResNeXt for classification and precise localization. During the training process of the Faster RCNN algorithm, the EOA was utilized to optimize the hyperparameter of the ResNeXt model which increased the segmentation results and reduced the loss function. The experimental outcome of the EOADL-BIS algorithm was tested on distinct benchmark medical image databases. The experimental results stated the greater efficiency of the EOADL-BIS algorithm compared to other DL-based segmentation approaches.

This study was carried to assess the radiological health risks associated with occupational exposure to natural radioactivity in some of ceramic raw materials and building materials. The concentrations of Ra-226, Th-232, and K-40 were measured using a gamma ray spectrometer equipped with a hyper pure germanium (HPGe) detector. In the present work, the mean values of Ra-226 for fly ash, bauxite, ceramic colors and marble are higher than the world average value of 35 (Bq/kg). The mean values of Th-232 for clay, fly ash, bauxite, ceramic colors, marble and granite are higher than the world average value of 30 (Bq/kg). For K-40, field spar, ceramic colors, marble and granite have mean values that higher than the world average value of 400 (Bq/kg). The radiological health hazard parameters such as radium equivalent activity, absorbed dose rate, annual effective dose rate, excess life time cancer risk, external hazard index and internal hazard index were calculated based on the mean values of these radionuclides. The maximum values of radium equivalent activity, absorbed dose rate, annual effective dose rate, excess life time cancer risk, external hazard index and internal hazard index were 3299.98 (Bq/kg), 4429.658 (nGy/h), 3.703 (mSv/y), 12.962, 8.92 and 15.97; respectively. Also, the equivalent and effective doses due to occupational exposure were calculated with Dose Conversion Factors for External Exposure (DFEXT code).

Depression detection is the procedure of recognizing the individuals exhibiting depression symptoms, which is a mental illness that is characterized by hopelessness, feelings of sadness, persistence and loss of interest in day-to-day activities. Depression detection in Social Networking Sites (SNS) is a challenging task due to the huge volume of data and its complicated variations. However, it is feasible to detect the depression of the individuals by examining the user-generated content utilizing Deep Learning (DL), Machine Learning (ML) and Natural Language Processing (NLP) approaches. These techniques demonstrate optimum outcomes in early and accurate detection of depression, which in turn can support in enhancing the treatment outcomes and avoid more complications related to depression. In order to provide more insights, both ML and DL approaches possibly offer unique features. These features support the evaluation of unique patterns that are hidden in online interactions and address them to expose the mental state amongst the SNS users. In the current study, we develop the Modified Arithmetic Optimization Algorithm with Deep Learning for Depression Detection in Twitter Data (MAOADL-DDTD) technique. The presented MAOADL-DDTD technique focuses on identification and classification of the depression sentiments in Twitter data. In the presented MAOADL-DDTD technique, the noise in the tweets is pre-processed in different ways. In addition to this, the Glove word embedding technique is used to extract the features from the preprocessed data. For depression detection, the Sparse Autoencoder (SAE) model is applied. The MAOA is used for optimum hyperparameter tuning of the SAE approach so as to optimize the performance of the SAE model, which helps in accomplishing better detection performance. The MAOADL-DDTD algorithm is simulated using the benchmark database and experimentally validated. The experimental values of the MAOADL-DDTD methodology establish its promising performance over another recent state-of-the-art approaches.

Abstract Melanoma brain metastases (MBM) are clinically challenging to treat and exhibit variable responses to immune checkpoint therapies. Prior research suggests that MBM exhibit poor tumor immune responses and are enriched in oxidative phosphorylation. Here, we report results from a multi-omic analysis of a large, real-world melanoma cohort. MBM exhibited lower interferon-gamma (IFNγ) scores and T cell-inflamed scores compared to primary cutaneous melanoma (PCM) or extracranial metastases (ECM), which was independent of tumor mutational burden. Among MBM, there were fewer computationally inferred immune cell infiltrates, which correlated with lower TNF and IL12B mRNA levels. Ingenuity pathway analysis (IPA) revealed suppression of inflammatory responses and dendritic cell maturation pathways. MBM also demonstrated a higher frequency of pathogenic PTEN mutations and angiogenic signaling. Oxidative phosphorylation (OXPHOS) was enriched in MBM and negatively correlated with NK cell and B cell-associated transcriptomic signatures. Modulating metabolic or angiogenic pathways in MBM may improve responses to immunotherapy in this difficult-to-treat patient subset.

Rough set has an important role to deal with uncertainty objects. The aim of this article is to introduce some kinds of generalization for rough sets through minimal neighborhoods using special kinds of binary relations. Moreover, four different types of dual approximation operators will be constructed in terms of minimal neighborhoods. The comparison between these types of approximation operators is discussed. Some new kinds of topological structures induced by minimal neighborhoods are established and some of their properties are studied. Finally, we give a comparison between these topologies that help for determining the major components of COVID-19 infections. In this application, the components of infections help the expert in decision making in medicine.

When scale builds up in a transmission pipeline, it narrows the pipe’s interior and causes losses in both power and efficiency. A noninvasive instrument based on gamma-ray attenuation is one of the most reliable diagnostic procedures for determining volumetric percentages in a variety of circumstances. A system with a NaI detector and dual-energy gamma generator simulations (241Am and 133 Ba radioisotopes) is recommended for simulating a volume percentage detection system utilizing Monte Carlo N particle (MCNP). Three-phase flow consisting of oil, water, and gas moves through a scaled pipe of variable wall thicknesses in a stratified flow regime with changing volume percentages. After gamma rays are emitted from one end of the pipe, a detector take in the photons coming from the other end. Four temporal features, including kurtosis and mean value of the square root (MSR), skewness, and waveform length (WL) picked up by the detector, were thus obtained. By training two GMDH neural networks with the aforementioned inputs, it is possible to forecast volumetric percentages with an RMSE of less than 0.90 and independently of scale thickness. The low error value, simplicity of the system, and reduction of design costs ensures the effectiveness of the suggested method and the advantages of employing this approach in the petroleum and petrochemical industries.

The neurological ailment known as Parkinson’s disease (PD) affects people throughout the globe. The neurodegenerative PD-related disorder primarily affects people in middle to late life. Motor symptoms such as tremors, muscle rigidity, and sluggish, clumsy movement are common in patients with this disorder. Genetic and environmental variables play significant roles in the development of PD. Despite much investigation, the root cause of this neurodegenerative disease is still unidentified. Clinical diagnostics rely heavily on promptly detecting such irregularities to slow or stop the progression of illnesses successfully. Because of its direct correlation with brain activity, electroencephalography (EEG) is an essential PD diagnostic technique. Electroencephalography, or EEG, data are biomarkers of brain activity changes. However, these signals are non-linear, non-stationary, and complicated, making analysis difficult. One must often resort to a lengthy human labor process to accomplish results using traditional machine-learning approaches. The breakdown, feature extraction, and classification processes are typical examples of these stages. To overcome these obstacles, we present a novel deep-learning model for the automated identification of Parkinson’s disease (PD). The Gabor transform, a standard method in EEG signal processing, was used to turn the raw data from the EEG recordings into spectrograms. In this research, we propose densely linked bidirectional long short-term memory (DLBLSTM), which first represents each layer as the sum of its hidden state plus the hidden states of all layers above it, then recursively transmits that representation to all layers below it. This study’s suggested deep learning model was trained using these spectrograms as input data. Using a robust sixfold cross-validation method, the proposed model showed excellent accuracy with a classification accuracy of 99.6%. The results indicate that the suggested algorithm can automatically identify PD.

Middle East and North Africa (MENA) countries are experiencing rapid population growth, so water and electricity consumption plays a crucial role in the sustainable development of these countries. To overcome the water scarcity and electricity problems facing the MENA region, the developed cooling/preheating small PVT-RO desalination plants have been proposed as a practical solution. To achieve sustainable water and energy development in the MENA region, this study presents a commendable and highly efficient renewable energy project for freshwater production and electricity generation to solve the energy crisis and water scarcity in the MENA countries. Therefore, this study aims to develop a cooling/preheating small PVT-RO desalination plant to facilitate freshwater supply to remote regions and produce electricity. This was done by connecting photovoltaic/thermal (PVT) collectors with reverse osmosis (RO) desalination systems, where seawater is used as a medium to cool photovoltaic cells to increase electric power generation and at the same time recover thermal energy and use it in the initial heating of feed seawater before it is fed into the RO plants, thus increasing its productivity. The results indicate that using the photovoltaic thermal panels as preheating units will lead to a 0.135 kWh/m3 reduction in the rate of specific electricity consumption for the RO desalination plant, as well as increase the electricity generation from PVT panels by a rate of 8%. The economic feasibility presented that the proposed developed cooling/preheating small PVT-RO desalination plant represents an effective technology that reduced the freshwater cost by a rate of 49.5%.

Spread across Twente and its neighbouring regions in the east of the Netherlands, a network of 20 profile soil moisture and temperature (5, 10, 20, 40, and 80 cm depths) monitoring stations was established in 2009. Field campaigns have been conducted covering the growing seasons of 2009, 2015, 2016, and 2017, during which soil sampling rings and handheld probes were used to measure the top 5 cm volumetric soil moisture content (VSM) of 28 fields near 12 monitoring stations. In this paper, we describe the design of the monitoring network and the field campaigns, adopted instrumentation, experimental setup, field sampling strategies, and the development of sensor calibration functions. Maintenance and quality control procedures and issues specific to the Twente network are discussed. Moreover, we provide an overview of open third-party datasets (i.e. land cover/use, soil information, elevation, groundwater, and meteorological observations) that can support the use and analysis of the Twente soil moisture and temperature datasets beyond the scope of this contribution. An indication for the spatial representativeness of the permanent monitoring stations is provided through comparisons of the 5 cm station measurements with the top 5 cm field-averaged VSM derived from the field campaign measurements. The results reveal in general reasonable agreements and root mean squared errors that are dominated by underestimations of the field-averaged VSM, which is particularly apparent for the grass fields and is strong after heavy rain. Further, we discuss the prospects the datasets offer to investigate (i) the reliability of soil moisture references that serve the development and validation of soil moisture products, and (ii) the water and energy exchanges across the groundwater–vadose-zone–atmosphere continuum within a lowland environment in a changing climate. The datasets discussed are publicly available at https://doi.org/10.17026/dans-znj-wyg5 (Van der Velde et al., 2022).