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المقدمة :إنتان الشنت هو ظهور أعراض وعلامات التهاب السحايا عند مريض أجريت له عملية زرع جهاز شنت إضافة إلى ظهور علامات سوء وظيفة الشنت . يعد انتان الشنت من المضاعفات الخطيرة التي تسبب عجزاً عصبياً قد يكون دائماً وقد يهدد الحياة وعلاج ذلك يكلف مبالغ طائلة )في الولايات المتحدة يصل إلى 30 ألف دولار). يتراوح معدل حدوث إنتان الشنت من 0.3 إلى 22.9% (2-1) تشمل عوامل الخطورة لحدوث إنتان الشنت ما يلي: خبرة الجراح، مدة العمل الجراحي، التعامل مع الجهاز أثناء العملية و اكتظاظ غرفة العمليات (3-4) الهدف من الدراسة: التحقق فيما إذا كان غسيل الشنت بالصادات الحيوية قبل زرعه يمكن أن يقلل من معدل حدوث إنتان الشنت . الطرق والأساليب : خضع 258 مريضاً لعملية زرع جهاز شنت في قسم الجراحة العصبية في مشفى تشرين الجامعي على مدار خمس سنوات من 2017 حتى 2022 ، قام بإجراء هذه العمليات جراحون متمرسون. تم تقسيم المرضى إلى مجموعتين : المجموعة الأولى (مجموعة الغسل)تضمنت 86 مريض تم فيها استخدام الفانكومايسين الممدد بالسيروم الفيزيولوجي لغسل الأجهزة المستخدمة قبل زراعتها المجموعة الثانية (مجموعة الشاهد) تضمنت 172 مريض تم في هذه المجموعة زرع الأجهزة بدون غسيل .

While cognitive theory has advanced several candidate frameworks to explain attentional entrainment, the neural basis for the temporal allocation of attention is unknown. Here we present a new model of attentional entrainment guided by empirical evidence obtained using a cohort of 50 artificial brains. These brains were evolved in silico to perform a duration judgment task similar to one where human subjects perform duration judgments in auditory oddball paradigms. We found that the artificial brains display psychometric characteristics remarkably similar to those of human listeners and exhibit similar patterns of distortions of perception when presented with out-of-rhythm oddballs. A detailed analysis of mechanisms behind the duration distortion suggests that attention peaks at the end of the tone, which is inconsistent with previous attentional entrainment models. Instead, the new model of entrainment emphasizes increased attention to those aspects of the stimulus that the brain expects to be highly informative. [ABSTRACT FROM AUTHOR]

This paper employs artificial neural networks (ANNs) in estimating and controlling the speed of a separately excited DC motor, which is also one of the most essential modern techniques used in control applications and to enhance the effectiveness of separately excited DC motor speed control. For speed control of a separately excited DC motor, the paper compares the performance of the conventional proportional integral (PI) and artificial neural networks (ANNs). A neural network (NNs) based controller is an extremely appreciated technology for accomplishing high-recital speed control. The simulation results are expose to demonstrate the efficacy and advantage of the control system of a separately excited DC motor (SEDCM) using ANNs over the traditional control arrangement. The integration of (ANNs) has shown significant improvements in performance in speed control of DC motors separately excited, chiefly in dynamic response and robustness in contradiction of load variations. This technique leverages the adaptive competences of ANNs to improve traditional control methods. In this manuscript, two controller for speed control of SEDCM are designed and simulated using matlab Simulink program. The first one is the PI controller and the second is the ANN controller, the results of the ANN controller based speed control of the SEDCM give quick response, low overshot, small settling time, zero steady state error when sudden change happened in reference speed or load torque. In addition, as compared with the results of PI controller which give slow response, high overshot, large settling time under the same conditions. [ABSTRACT FROM AUTHOR]

Background/Objective: This study examined the anti-obesity effect of royal jelly (RJ) in rats fed with a high-fat diet by targeting the major pathways involved in adipogenesis and lipolysis. In addition, it examined whether this effect is AMPK-dependent. Methods: Five groups of adult male albino rats were used (n = 6 each as 1); the control rats were fed with a normal diet (2.9 kcal), and the other groups were as follows: control + RJ (300 mg/kg), HFD (4.75 kcal), HFD + RJ (300 mg/kg), and HFD + RJ (300 mg/kg) + dorsomorphin (an AMPK inhibitor) (0.2 mg/kg). Results: RJ was administered orally to all rats. With no changes in food and energy intake, RJ significantly reduced gains in body weight, fat weight, body mass index (BMI), the Lee index, abdominal circumference (AC), and the adiposity index (AI). It also reduced fasting glucose and insulin levels, HOMA-IR, and the circulatory levels of free fatty acids (FFAs), triglycerides, cholesterol, and LDL-c in the HFD-fed rats. RJ also increased serum glycerol levels and adiponectin levels, but reduced the serum levels of leptin, IL-6, and TNF-α. Moreover, RJ reduced the secretion of IL-6 and TNF-α from isolated WAT. At the tissue level, the HFD + RJ rats exhibited a smaller adipocyte size compared to the HFD rats. At the molecular level, RJ increased the phosphorylation of AMPK, SREBP1, and ACC-1 and increased the mRNA and protein levels of HSL and ATG in the WAT of the HFD rats. In concomitance, RJ increased the mRNA levels of PGC-α1, reduced the protein levels of PPARγ, and repressed the transcriptional activities of PPARγ, SREBP1, and C/EBPαβ in the WAT of these rats. All the aforementioned effects of RJ were prevented by co-treatment with dorsomorphin. Conclusions: RJ exerts a potent anti-obesity effect in rats that is mediated by the AMPk-dependent suppression of WAT adipogenesis and the stimulation of lipolysis. [ABSTRACT FROM AUTHOR]

Concerning the oversight and safeguarding of aquatic environments, it is necessary to ascertain the quantity of fish, their size, and their distribution. Many deep learning (DL), artificial intelligence (AI), and machine learning (ML) techniques have been developed to oversee and safeguard the fish species. Still, all the previous work had some limitations, such as a limited dataset, only binary class categorization, only employing one technique (ML/DL), etc. Therefore, in the proposed work, the authors develop an architecture that will eliminate all the limitations. Both DL and ML techniques were used in the suggested framework to identify and categorize multiple classes of the salinity and freshwater fish species. Two different datasets of fish images with thirteen fish species were employed in the current research. Seven CNN architectures were implemented to find out the important features of the fish images. Then, seven ML classifiers were utilized in the suggested work to identify the binary class (freshwater and salinity) of fish species. Following that, the multiclass classification of thirteen fish species was evaluated through the ML algorithms, where the present model diagnosed the freshwater or salinity fish in the specific fish species. To achieve the primary goals of the proposed study, several assessments of the experimental data are provided. The results of the investigation indicated that DenseNet121, EfficientNetB0, ResNet50, VGG16, and VGG19 architectures of the CNN with SVC ML technique achieved 100% accuracy, F1-score, precision, and recall for binary classification (freshwater/salinity) of fish images. Additionally, the ResNet50 architecture of the CNN with SVC ML technique achieved 98.06% and 100% accuracy for multiclass classification (freshwater and salinity fish species) of fish images. However, the proposed pipeline can be very effective in sustainable fish management in fish identification and classification. [ABSTRACT FROM AUTHOR]

Three-dimensional printable concrete (3DPC) has become increasingly popular in the building and architecture industries due to its low cost and fast design. Currently, there is great interest in the mix design methods and mechanical properties of 3DPC, particularly in relation to yield stress analysis. The ability to extrude and build 3D-printed objects can be significantly affected by factors such as the rate of extrusion, nozzle size, and type of pumps used. It has been observed that a yield stress lower than 1.5 to 2.5 kPa is not sufficient to maintain the shape stability of concrete, while a yield stress above this range can limit the material's extrudability. Furthermore, the strength properties of 3DPC are influenced by factors such as changes in yield stress and superplasticiser dosages. To meet the high mechanical strength and durability requirements of 3DPC in the construction industry, it is essential to analyse the material's early-age mechanical properties. However, the development of standardised test methods for 3DPC is still deficient. To address this issue, a bibliometric analysis was conducted to comprehensively review the diverse test methods and mechanical characteristics of 3DPC with different mix proportions. To produce high-performance concrete from various additives and waste materials, it is critical to have a basic understanding of the hydration processes of 3DPC. Moreover, a detailed analysis of the environmental impact and energy efficiency of 3DPC is necessary for its widespread implementation. This review article will highlight the recent trends, upcoming challenges, and benefits of using 3DPC. It serves as a taxonomy to navigate the field of 3DPC towards sustainable development. [ABSTRACT FROM AUTHOR]

This study aimed to assess organizational health (OH) in secondary schools in Jeddah district in Saudi Arabia. A second aim of the study was to compare the organizational health of these schools according to their rankings on student achievement tests, school type and the nature of the respondents' work. The Organizational Health Inventory (OHI), developed by Hoy and Feldman (1987), was used to examine OH in the subject schools. The (138) secondary schools in Jeddah district were classified into three main categories (high, average, low) based on their students' results on the achievement test for science colleges, which is given annually across the nation by the National Center for Assessment in Higher Education (NCAHE). With the school and not the respondent selected as the unit of research, 20 random schools from each category were compared using the ratings of "high"-achieving, "average"- achieving and "low"-achieving schools, as measured by the NCAHE, on the 7 dimensions of the OHI. All the principals and teachers working in the three selected representative school categories were approached in the process of data collection. The secondary schools' OH scores were found relatively high. The high-achieving schools had higher OH scores than the schools in other two categories. Moreover, the average-achieving schools outperformed the low-achieving schools on overall OH scores. Findings also showed that the private schools had healthier climates than the public schools. Schools can help improve their student learning process and academic attainment by improving the health of their organizational environment.

An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches.

The rapid growth of Internet of Things (IoT) devices and the increasing need for low-latency and high-throughput applications have led to the introduction of distributed edge computing. Flying fog computing is a promising solution that can be used to assist IoT networks. It leverages drones with computing capabilities (e.g., fog nodes), enabling data processing and storage closer to the network edge. This introduces various benefits to IoT networks compared to deploying traditional static edge computing paradigms, including coverage improvement, enabling dense deployment, and increasing availability and reliability. However, drones’ dynamic and mobile nature poses significant challenges in task offloading decisions to optimize resource utilization and overall network performance. This work presents a novel offloading model based on dynamic programming explicitly tailored for flying fog-based IoT networks. The proposed algorithm aims to intelligently determine the optimal task assignment strategy by considering the mobility patterns of drones, the computational capacity of fog nodes, the communication constraints of the IoT devices, and the latency requirements. Extensive simulations and experiments were conducted to test the proposed approach. Our results revealed significant improvements in latency, availability, and the cost of resources.

Breast cancer, a leading cause of female mortality worldwide, poses a significant health challenge. Recent advancements in deep learning techniques have revolutionized breast cancer pathology by enabling accurate image classification. Various imaging methods, such as mammography, CT, MRI, ultrasound, and biopsies, aid in breast cancer detection. Computer-assisted pathological image classification is of paramount importance for breast cancer diagnosis. This study introduces a novel approach to breast cancer histopathological image classification. It leverages modified pre-trained CNN models and attention mechanisms to enhance model interpretability and robustness, emphasizing localized features and enabling accurate discrimination of complex cases. Our method involves transfer learning with deep CNN models—Xception, VGG16, ResNet50, MobileNet, and DenseNet121—augmented with the convolutional block attention module (CBAM). The pre-trained models are finetuned, and the two CBAM models are incorporated at the end of the pre-trained models. The models are compared to state-of-the-art breast cancer diagnosis approaches and tested for accuracy, precision, recall, and F1 score. The confusion matrices are used to evaluate and visualize the results of the compared models. They help in assessing the models’ performance. The test accuracy rates for the attention mechanism (AM) using the Xception model on the “BreakHis” breast cancer dataset are encouraging at 99.2% and 99.5%. The test accuracy for DenseNet121 with AMs is 99.6%. The proposed approaches also performed better than previous approaches examined in the related studies.

The Internet of Things (IoT) has established itself as a multibillion-dollar business in recent years. Despite its obvious advantages, the widespread nature of IoT renders it insecure and a potential target for cyber-attacks. Furthermore, these devices broad connectivity and dynamic heterogeneous nature can open up a new surface of attack for refined malware attacks. There is a critical need to protect the IoT environment from such attacks and malware. Therefore this research aims to propose an intelligent, SDN-enabled hybrid framework leveraging Cuda Long Short Term Memory Gated Recurrent Unit (cuLSTMGRU) for efficient threat detection in IoT environments. To properly assess the proposed system, a state-of-the-art IoT-based dataset and standard evaluation metrics were used. The proposed model achieved 99.23 % detection accuracy with a low false-positive rate. For further verification, we compare the proposed model results with two of our constructed models (i.e., cuBLSTM and cuGRUDNN) and current benchmark algorithms. The proposed model outclassed the other models regarding speed efficiency, detection accuracy, precision, and other standard evaluation metrics. Finally, the proposed work employed 10-fold cross-validation to ensure that the results were completely unbiased.

Internet of Things (IoT) is an instantly exacerbated communication technology that is manifesting miraculous effectuation to revolutionize conventional means of network communication. The applications of IoT are compendiously encompassing our prevalent lifestyle and the integration of IoT with other technologies makes this application spectrum even more latitudinous. However, this admissibility also introduces IoT with a pervasive array of imperative security hazards that demands noteworthy solutions to be swamped. In this scientific study, we proposed Deep Learning (DL) driven Software Defined Networking (SDN) enabled Intrusion Detection System (IDS) to combat emerging cyber threats in IoT. Our proposed model (DNNLSTM) is capable to encounter a tremendous class of common as well as less frequently occurring cyber threats in IoT communications. The proposed model is trained on CICIDS 2018 dataset, and its performance is evaluated on several decisive parameters i.e Accuracy, Precision, Recall, and F1-Score. Furthermore, the designed framework is analytically compared with relevant classifiers, i.e., DNNGRU, and BLSTM for appropriate validation. An exhaustive performance comparison is also conducted between the proposed system and a few preeminent solutions from the literature. The proposed design has circumvented the existing literature with unprecedented performance repercussions such as 99.55% accuracy, 99.36% precision, 99.44% recall, and 99.42% F1-score.

ObjectiveTo provide estimates on the regional and national burden of tracheal, bronchus, and lung (TBL) cancer and its attributable risk factors from 1990 to 2019 in the North Africa and Middle East (NAME) region.Methods and materialsThe Global Burden of Disease (GBD) 2019 data were used. Disability-adjusted life years (DALYs), death, incidence, and prevalence rates were categorized by sex and age groups in the NAME region, in 21 countries, from 1990 to 2019. Decomposition analysis was performed to calculate the proportion of responsible factors in the emergence of new cases. Data are presented as point estimates with their 95% uncertainty intervals (UIs).ResultsIn the NAME region, TBL cancer caused 15,396 and 57,114 deaths in women and men, respectively, in 2019. The age-standardized incidence rate (ASIR) increased by 0.7% (95% UI -20.6 to 24.1) and reached 16.8 per 100,000 (14.9 to 19.0) in 2019. All the age-standardized indices had a decreasing trend in men and an increasing trend in women from 1990 to 2019. Turkey (34.9 per 100,000 [27.6 to 43.5]) and Sudan (8.0 per 100,000 [5.2 to 12.5]) had the highest and lowest age-standardized prevalence rates (ASPRs) in 2019, respectively. The highest and lowest absolute slopes of change in ASPR, from 1990 to 2019, were seen in Bahrain (-50.0% (-63.6 to -31.7)) and the United Arab Emirates (-1.2% (-34.1 to 53.8)), respectively. The number of deaths attributable to risk factors was 58,816 (51,709 to 67,323) in 2019 and increased by 136.5%. Decomposition analysis showed that population growth and age structure change positively contributed to new incident cases. More than 80% of DALYs could be decreased by controlling risk factors, particularly tobacco use.ConclusionThe incidence, prevalence, and DALY rates of TBL cancer increased, and the death rate remained unchanged from 1990 to 2019. All the indices and contribution of risk factors decreased in men but increased in women. Tobacco is still the leading risk factor. Early diagnosis and tobacco cessation policies should be improved.

Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks. This paper presents an Enhanced Slime Mould Optimization with Deep-Learning-based Resource Allocation Approach (ESMOML-RAA) in UAV-enabled wireless networks. The presented ESMOML-RAA technique aims to efficiently accomplish computationally and energy-effective decisions. In addition, the ESMOML-RAA technique considers a UAV as a learning agent with the formation of a resource assignment decision as an action and designs a reward function with the intention of the minimization of the weighted resource consumption. For resource allocation, the presented ESMOML-RAA technique employs a highly parallelized long short-term memory (HP-LSTM) model with an ESMO algorithm as a hyperparameter optimizer. Using the ESMO algorithm helps properly tune the hyperparameters related to the HP-LSTM model. The performance validation of the ESMOML-RAA technique is tested using a series of simulations. This comparison study reports the enhanced performance of the ESMOML-RAA technique over other ML models.

On the one hand, the Internet of Medical Things (IoMT) in healthcare systems has emerged as a promising technology to monitor patients’ health and provide reliable medical services, especially in remote and underserved areas. On the other hand, in disaster scenarios, the loss of communication infrastructure can make it challenging to establish reliable communication and to provide timely first aid services. To address this challenge, unmanned aerial vehicles (UAVs) have been adopted to assist hospital centers in delivering medical care to hard-to-reach areas. Despite the potential of UAVs to improve medical services in emergency scenarios, their limited resources make their security critical. Therefore, developing secure and efficient communication protocols for IoMT networks using UAVs is a vital research area that can help ensure reliable and timely medical services. In this paper, we introduce a novel Stackelberg security-based game theory algorithm, named Stackelberg ad hoc on-demand distance vector (SBAODV), to detect and recover data affected by black hole attacks in IoMT networks using UAVs. Our proposed scheme utilizes the substantial Stackelberg equilibrium (SSE) to formulate strategies that protect the system against attacks. We evaluate the performance of our proposed SBAODV scheme and compare it with existing routing schemes. Our results demonstrate that our proposed scheme outperforms existing schemes regarding packet delivery ratio (PDR), networking load, throughput, detection ratio, and end-to-end delay. Specifically, our proposed SBAODV protocol achieves a PDR of 97%, throughput ranging from 77.7 kbps to 87.3 kbps, and up to 95% malicious detection rate at the highest number of nodes. Furthermore, our proposed SBADOV scheme offers significantly lower networking load (7% to 30%) and end-to-end delay (up to 30%) compared to existing routing schemes. These results demonstrate the efficiency and effectiveness of our proposed scheme in ensuring reliable and secure communication in IoMT emergency scenarios using UAVs.

Sensory neurons have the capacity to produce, release, and respond to acetylcholine (ACh), but the functional role of cholinergic systems in adult mammalian peripheral sensory nerves has not been established. Here, we have reported that neurite outgrowth from adult sensory neurons that were maintained under subsaturating neurotrophic factor conditions operates under cholinergic constraint that is mediated by muscarinic receptor-dependent regulation of mitochondrial function via AMPK. Sensory neurons from mice lacking the muscarinic ACh type 1 receptor (M1R) exhibited enhanced neurite outgrowth, confirming the role of M1R in tonic suppression of axonal plasticity. M1R-deficient mice made diabetic with streptozotocin were protected from physiological and structural indices of sensory neuropathy. Pharmacological blockade of M1R using specific or selective antagonists, pirenzepine, VU0255035, or muscarinic toxin 7 (MT7) activated AMPK and overcame diabetes-induced mitochondrial dysfunction in vitro and in vivo. These antimuscarinic drugs prevented or reversed indices of peripheral neuropathy, such as depletion of sensory nerve terminals, thermal hypoalgesia, and nerve conduction slowing in diverse rodent models of diabetes. Pirenzepine and MT7 also prevented peripheral neuropathy induced by the chemotherapeutic agents dichloroacetate and paclitaxel or HIV envelope protein gp120. As a variety of antimuscarinic drugs are approved for clinical use against other conditions, prompt translation of this therapeutic approach to clinical trials is feasible.

The main perfect of this composition is to discover the stylish version to prognosticate the cost of the inventory request. During the procedure of analyzing the colorful ways and variables to remember, we plant that approaches similar as Random woodland, machine help Vector were not absolutely exploited. On this composition, we will introduce and assessment a in addition practicable gadget to prognosticate the motion of shares with lesser delicacy. The first issue we looked at turned into the previous time's stock price dataset. The dataset has been preprocessed and refined for actual analysis. For this reason, our composition can even focus on preprocessing the raw data of the dataset. 2nd, after preprocessing the facts, we are able to observe the use of the arbitrary wood, we can aid the vector machine on the dataset and the results it generates. Similarly, the proposed composition examines the use of the soothsaying device in actual surrounds and the problems related to the delicacy of the overall values handed. The composition additionally provides a system literacy version for prognosticating the lifestyles of shares in a aggressive request. Predicting the success of shares might be a main asset for stock request institutions and could give actual effects to the troubles facing equity investors. By Using Stock Prediction algorithm overall accuracy is 80.3%.

Danah Aljaafari,1 Salam Almustafa,2 Abdulrahman Saleh Ali,3 Hosam Aldalbahi,3 Norah Ibrahim Albahli,3 Feras AlSulaiman,1 Anas Al dehailan,1 Majed Alabdali1 1Department of Neurology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; 2College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; 3Department of Neurology, King Fahad Medical City, Riyadh, Saudi ArabiaCorrespondence: Danah AljaafariDepartment of Neurology, College of Medicine Imam Abdulrahaman Bin Faisal University, 2835 King Faisal Road, Dammam, 34212, Kingdom of Saudi ArabiaTel +966 503864084Background: Miller Fisher syndrome (MFS), a triad of ophthalmoplegia, areflexia and ataxia, is one of the regional variants of Guillain-Barré syndrome (GBS) that might account for a quarter of all cases of GBS, especially in Asian countries. There is history of an antecedent upper respiratory tract infection in up to two thirds of MFS cases. However, association of MFS in adults and pneumonia is rarely reported and in those cases causative pathogen was Mycoplasma pneumoniae. To our knowledge, association of MFS and ventilator-associated pneumonia has never been reported. So, we hereby report the first case of MFS which followed ventilator-associated pneumonia (VAP).Case Report: We report case of a 22-year-old male who was known to have temporal lobe epilepsy and mental retardation. He presented with status epilepticus. He was sedated and put on mechanical ventilation. Two days later, he developed a fever associated with increased tracheobronchial secretions and new infiltrates on chest X-ray. Diagnosis of VAP was made. Upon improvement, he was extubated and shifted out of ICU. Ten days after the onset of fever, he developed gradual onset bulbar weakness and ataxia. On examination, he had generalized areflexia and ataxia. CSF analysis showed cytoalbuminic dissociation. Antibodies against ganglioside complex were elevated. Diagnosis of sero-negative MFS was made, and intravenous immunoglobulin (IVIG) was started. He improved remarkably within two days.Conclusion: MFS is immune-mediated entity which is usually triggered by upper respiratory tract infection but in rare cases it can be consequence of pneumonia including VAP. Further research is needed to establish link between MFS and VAP.Keywords: Guillain-Barré syndrome, Miller Fisher syndrome, ventilator-associated pneumonia, Pseudomonas aeruginosa

The domestication of animals and the cultivation of crops have been essential to human development throughout history, with the agricultural sector playing a pivotal role. Insufficient nutrition often leads to plant diseases, such as those affecting rice crops, resulting in yield losses of 20–40% of total production. These losses carry significant global economic consequences. Timely disease diagnosis is critical for implementing effective treatments and mitigating financial losses. However, despite technological advancements, rice disease diagnosis primarily depends on manual methods. In this study, we present a novel self-attention network (SANET) based on the ResNet50 architecture, incorporating a kernel attention mechanism for accurate AI-assisted rice disease classification. We employ attention modules to extract contextual dependencies within images, focusing on essential features for disease identification. Using a publicly available rice disease dataset comprising four classes (three disease types and healthy leaves), we conducted cross-validated classification experiments to evaluate our proposed model. The results reveal that the attention-based mechanism effectively guides the convolutional neural network (CNN) in learning valuable features, resulting in accurate image classification and reduced performance variation compared to state-of-the-art methods. Our SANET model achieved a test set accuracy of 98.71%, surpassing that of current leading models. These findings highlight the potential for widespread AI adoption in agricultural disease diagnosis and management, ultimately enhancing efficiency and effectiveness within the sector.

To improve the adsorption of hydrogen sulfide (H2S) by using coconut shell-activated carbon xerogel (CSACX), we adopted the response surface methodology (RSM) with a central composite design (CCD). This material was created by incorporating a cross-linker agent, initiator agent, and polymer. The process of creating CSACX involved synthesizing coconut shell activated carbon into a wet gel using chemicals such as sodium alginate, calcium carbonate, glucono delta-lactone (GDL), and distilled water in a sol-gel method to obtain a xerogel. Afterward, the gel was dried in an oven at 60℃ for 24 hours. Subsequently, it was used as an adsorbent for the adsorption test. The adsorption test was conducted at two different initial concentrations of H2S, 25 ppm, and 50 ppm, to assess the effectiveness of H2S removal at different concentrations. In the RSM approach, we selected adsorption pressure (1-3 bar) and H2S flow rate (100-300 L/hr) as the process variables while maintaining a constant contact time (5 minutes), adsorbent weight (11 g) and temperature (30℃). The removal efficiency of H2S (%) was chosen as the response. Our findings showed that the optimum conditions for H2S removal were at 1 bar and 100 L/hr for 25 ppm of H2S and 1 bar and 100.3830 L/hr for 50 ppm of H2S. The model generated from RSM predicted that maximum H2S removal can be achieved at a lower pressure and flow rate for any H2S initial concentration. [ABSTRACT FROM AUTHOR]

Background:  To evaluate the radiant power of the light cure units (LCUs) in relation to their type, radiant exitance, number of years in clinical use, and condition of LCUs tips in governmental and public clinics in Dental Faculties in Sana'a City. Materials and Methods: LCUs were collected from different colleges at Sanaa City, Yemen, then LCU data as type, clinical age (< 1 year, between 1– 5 and ˃ 5-years), tip condition was visually inspected for damage and adhering debris, and the radiant exitance values of the tested LCUs. Radiant exitance values were subcategorized into three groups: < 400, 400– 850, and > 850 mW/cm², labeled as inadequate, marginal, and adequate radiant exitances, respectively. A Woodpecker radiometer was used with a mode lasting of 20 seconds was used with each LCU. Descriptive statistics of the different parameters were evaluated with SPSS version 25. One-way ANOVA and Mann–Whitney tests were performed to determine the mean difference between the groups with a significance value of ˂ 0.05 was considered. Results: Two hundred twenty-three LCUs were surveyed, and the majority were Light–emitting diode (LED). Forty-nine (21.9%), 117 (52.4%), 57 (25.6%) recorded lesser than, 400– 850, and more than 850 mW/cm², respectively. Radiant exitances of < year-old units were found to be higher than those of units used for ˃ 5 years with significant differences (p=0.001). The ANOVA test showed significant differences between the radiant exitance with clinical age and LCU tip conditions and a strong correlation p ˃ 0.050. Conclusion: LED curing lights were the most used in the tested Dental Faculties. More than half of the used LCU offered sufficient radiant exitance. Clinical age, the presence or absence of composite buildups, and damage to curing tips showed significantly affect radiant exitance values. [ABSTRACT FROM AUTHOR]

Biomass is an inexpensive adsorbent that has attracted considerable interest. The sol-gel process produced xerogel from palm kernel shell biochar (PKSB). This study aimed to synthesize and characterize palm kernel shell biochar xerogel (PKSBX) and compare it with commercial (AC). The synthesized xerogel, raw material, and AC were characterized using different characterization, including thermogravimetric analysis (TGA), X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Brunauer-Emmett-Teller (BET), and Scanning Electron Microscopy (SEM). The FTIR spectrum analysis showed a wide range of bonds and confirmed the presence of C = C alkenes, amines N-H, and aromatic C-H functional groups. TGA analysis of samples was conducted at 10 ™/min. The thermal degradation of the sample undergoes several setups of loss mass. The degrades occurred between 50200 ™ first setups, second between 200-700 ™, and third setups between 950-1000 ™. The surface morphological structure of each sample has been defined and compared using SEM data, which is further confirmed by XRD data. On the basis of on the characterization findings, it can be determined that the xerogel obtained from the synthesis process using PKSB as the raw material exhibits favorable characteristics for its potential usage as an adsorbent. [ABSTRACT FROM AUTHOR]