Ultra-short-term power load forecasting is beneficial to improve the economic efficiency of power systems and ensure the safe and stable operation of power grids. As the volatility and randomness of loads in power systems, make it difficult to achieve accurate and reliable power load forecasting, a sequence-to-sequence based learning framework is proposed to learn feature information in different dimensions synchronously. A CNN_BiLSTM network is built in the encoder to extract the correlated timing features embedded in external factors affecting power loads. The parallel BiLSTM network is built in the decoder to mine the power load timing information in different regions separately. The multi-headed attention mechanism is introduced to fuse the BiLSTM hidden layer state information in different components to further highlight the key information representation. The load forecastion results in different regions are output through the fully connected layer. The model proposed in this paper has the advantage of high forecastion accuracy through the example analysis of real power load data.

This work focuses on the behaviours of anthracite under differential cyclic loading (DCL), also examining the impact of wet and dry cycles on behaviour of coal pillars in coal mine based underground water reservoirs (CMUWR). Seven sets of coal samples and processed coal flakes were subjected to wet and dry treatments. Mechanical tests were conducted using two DCL paths (rapid loading and slow unloading and vice versa). Several parameters, incl. Poisson’s ratio, damping ratio, strain growth rate, and phase shift were analysed. Compared to slow loading and fast loading demonstrated higher damping ratio, strain growth rate, hysteresis ratio, and hysteresis time probability distributions. Moreover, the number of cracks and crack widths observed after wet and dry cycling of the flakes were directly proportional to the number of wet and dry cycles undergone. These findings provide valuable insights for design and safety considerations in coal pillar dam stability within underground reservoirs. By understanding the mechanical properties of coal under wet and dry cyclic loading, engineers and researchers can make informed decisions to enhance the stability and safety of coal pillar dams.

A document by Esmaeil Mehraeen. Click on the document to view its contents.

Nader Moeinvaziri1, Alireza Sadeghi11 Minimally Invasive Surgery Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

The Asian honeybee, Apis cerana, is an ecologically and economically important pollinator. Mapping its genetic variation is key to understanding population-level health, histories, and potential capacities to respond to environmental changes. However, most efforts to date were focused on single nucleotide polymorphisms (SNPs) based on a single reference genome, thereby ignoring larger-scale genomic variation. We employed long-read sequencing technologies to generate a chromosome-scale reference genome for the ancestral group of A. cerana. Integrating this with 525 resequencing datasets, we constructed the first pan-genome of A. cerana, encompassing almost the entire gene content. We found that 31.32% of genes in the pan-genome were variably present across populations, providing a broad gene pool for environmental adaptation. We identified and characterized structural variations (SVs) and found that they were not closely linked with SNP distributions, however, the formation of SVs was closely associated with transposable elements. Furthermore, phylogenetic analysis using SVs revealed a novel A. cerana ecological group not recoverable from the SNP data. Performing environmental association analysis identified a total of 44 SVs likely to be associated with environmental adaptation. Verification and analysis of one of these, a 330 bp deletion in the Atpalpha gene, indicated that this SV may promote the cold adaptation of A. cerana by altering gene expression. Taken together, our study demonstrates the feasibility and utility of applying pan-genome approaches to map and explore genetic feature variations of honeybee populations, and in particular to examine the role of SVs in the evolution and environmental adaptation of A. cerana.

Corynebacterium glutamicum is a useful microbe to produce succinic acid, a bio-based platform chemical, under anaerobic condition. The knock-out mutant of lactate dehydrogenase 1 gene (ΔldhA-6) and co-expression of succinic acid transporter (Psod:sucE- ΔldhA) were generated by using CRISPR-Cpf1 genome editing system. HAPC (hydrogen peroxide and acetic acid) pretreatment is a highly efficient method for enzymatic hydrolysis of softwood and the hydrolysate was used for production of succinic acid. In the 15% hydrolysate (Pinus densiflora), the best condition for ΔldhA mutant to produce succinic acid from the hydrolysate was confirmed to ferment 4% hydrolysate, resulted in 14.82 g L-1 succinic acid production for 6 h, which reached to 2.47 g L-1 h-1 productivity. No production of acetic acid and lactic acid was detected during the fermentation. The co-expression transformant, [Psod:sucE- ΔldhA], produced 17.70 g L-1 succinic acid in 6 h, presenting a productivity of 2.95 g L-1 h-1 on the 4% hydrolysate. In the fed-batch system, 39.67 g L-1 succinic acid was produced for 48 h. The yield of succinic acid from reducing sugars in the hydrolysate is approximately 56.71%, while the yield of succinic acid from glucose alone as the main substrate is approximately 84.4%. These results indicated that the production of succinic acid from softwood has potential applications in alternative biochemical processes, and minimizing the loss of sugars during enzymatic hydrolysis and fermentation can lead to more economic benefits in succinic acid production from lignocellulosic biomass.

Background We analyzed the effects of small-molecule antiviral treatment for coronavirus disease-2019 (COVID-19) Omicron strain in kidney transplant recipients. Methods We enrolled 140 kidney transplant patients admitted for COVID-19-related pneumonia were treated using small-molecule antivirals. Patients were divided into three groups: azvudine (n=62), paxlovid (n=49), and a combination of azvudine+paxlovid (A+P, n=29). Differences in clinical outcomes owing to COVID-19 infections were compared among three groups. Results Paxlovid group had a higher proportion of comorbid diabetes than the other two groups (P=0.032). There were differences in the clinical typing of the coronavirus , with the highest proportion of heavy and critical cases in the A+P group (35.5%). The immunosuppression prior to infection did not differ among the groups; however, after adjusting for immunosuppression during antiviral treatment, differences were observed. Of the 140 patients, 125 (89.29%) had fever, 114 (81.43%) had cough, and 66 (47.1%) had malaise. Combination of two or more symptoms were found in 90% patients. Mean length of hospitalization was slightly longer in the combination group than in the azvudine and paxlovid groups. Four deaths, all in the A+P group; five cases of loss of function, two in the paxlovid group and three in the A+P group; and acute kidney injury occurred in 30 patients with 7 in the azvudine, 17 in paxlovid, and 6 in A+P groups. Conclusion The use of small-molecule medications may be the optimal treatment approach; however, they should be modified based on the patients’ conditions, such as clinical symptoms, laboratory results, paraclinicals, and examinations.

Background and Purpose: Metabolic dysfunction-associated fatty liver disease (MAFLD) refers to fatty liver disease related to systemic metabolic dysregulation. The altered pharmacokinetics of drugs in MAFLD have been reported in several studies. Naproxen, a non-steroidal anti-inflammatory drug, is often used as an analgesic and anti-inflammatory agent. However, the pharmacokinetics of naproxen in MAFLD remains unknown. Experimental approach: High-fat diet (HFD)-fed rats were used to establish the MAFLD disease model. Serum triglyceride (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) were used as evaluation indicators. And hematoxylin-eosin staining was employed to observe the histomorphological changes in the liver to ensure a viable MAFLD model. After naproxen administration in MAFLD rats, the plasma concentrations of naproxen were measured by high-performance liquid chromatography. Key Results: Compared with normal diet (ND)-fed rats, the body weight, TC, and LDL-C were significantly increased in HFD-fed rats. And the hepatocytes in HFD-fed rats were significantly swollen, with most hepatocytes showing large vesicular steatosis, severe ballooning indicating successful modeling. The pharmacokinetic study of naproxen revealed that after naproxen administration, the mean plasma concentrations of naproxen were significantly lower in the HFD group compared to the ND group. Compared with ND-fed rats, the main pharmacokinetic parameters t1/2z, AUC0-t, AUC0-∞, and MRT0-∞ were significantly decreased, while CLz/F was significantly increased in HFD-fed rats. Conclusions & Implications: The pharmacokinetics of naproxen in the MAFLD rat model demonstrated reduced absorption, accelerated metabolism, and reduced retention time, which may provide some evidence for individualized treatment of patients with MAFLD.

Population dynamical modelling plays a crucial role in understanding ecological populations and making informed decisions for environmental management. However, existing software packages for dynamical system modelling often lack comprehensive integration of techniques and guidelines, limiting their practical usability. This paper introduces ecode, a novel package for modelling ecological populations and communities using ordinary differential equation systems, designed with a user-friendly framework. By following a three-cycle procedure, users can easily construct ecological models and explore their behaviours through a wide range of graphical, analytical, and numerical techniques. The package incorporates advanced techniques such as grid search methods and simulated annealing algorithms, enabling users to iteratively refine their models and achieve accurate predictions. Notably, ecode minimises external dependencies, ensuring robustness and reducing the risk of package failure caused by updates in dependencies. Overall, ecode serves as a valuable tool for ecological modelling, facilitating the exploration of complex ecological systems and the generation of informed predictions and management recommendations.

INTRODUCTIONThe mandible is a physiologically complex bone and is analogous to long bone. More than 30 lesions, including benign and malignant tumours, frequently occur in the mandible. In the spectrum of pathologies that present in the posterior mandible, ameloblastoma shows a marked prevalence. A variety of non-odontogenic solid malignancies can also occur in the mandible, but the mandible is not usually the primary site of diagnosis, rather it is known for metastatic involvement1.Small cell neuroendocrine carcinomas (SNECs) are poorly differentiated high-grade neuroendocrine tumours that usually present as primary lung tumours near the bronchial region. Head and neck events are infrequent, with the larynx being the most prevalent site. SNEC is diagnosed based on histology and confirmation of origin by immune histochemistry2. Primary SNECs in the head and neck region are sporadic tumours, with only a few cases reported in the English literature.This case report describes a primary SNEC diagnosed in the mandibular ramus of an elderly female patient who presented to the department with a swelling over the posterior mandible. The clinical presentation, and imaging findings initially were consistent with that of the benign odontogenic tumour, Ameloblastoma, until the immunohistochemistry reports were received.

CNNM2 genetic variant induced hereditary renal hypomagnesemia: A case reportJing Liu1, Yinjuan Zhang1, Lijun Liang1, Jinhai Ma1*1 The General Hospital of Ningxia Medical Universirty, Ningxia, China

Malicious attack is a potential threat for collision-free and connectivity-preserving formation control. In this paper, a predictor-based collision-free and connectivity-preserving resilient formation control strategy is presented for a class of nonlinear multi-agent systems under sensor deception attacks. The predictor states are designed to replace original states in the control strategy, and a novel attack compensator is constructed to suppress sensor deception attack. Prediction errors, instead of compromised errors, are introduced to update radial basis function neural networks (RBFNNs) weights. To achieve collision avoidance and connectivity preservation, a transformation function in logarithmic form is proposed. To avoid static and dynamic obstacles, an improved artificial potential function (APF) combined with their velocity information is constructed. Furthermore, to solve the local minimum in the combining of transformation function and APF, a virtual force is added to make agents get away. Based on the Lyapunov stability criterion, all closed-loop signals are bounded and all control objectives can be achieved. The simulation of a group of quadrotors has verified the effectiveness of the proposed resilient control strategy.

A document by Pourn varvani farahani. Click on the document to view its contents.

Under the framework of the step-function method, the stability of a nonlinear fuzzy hybrid control system combining an impulsive controller and a continuous state feedback controller is investigated. Both the two controllers are assumed to be subject to both actuator saturation and time-varying delays, which has received little attention if any, in the existing studies. A new assumption is established enabling the use of generalized sector conditions to tackle the double saturation, and the conservatism of the stability results is remarkably reduced thanks to the improved step-function method. The stability theorem proposed in this paper removes restriction on the time delays of both controllers, which can be also applied to wider scopes of systems, including hybrid control systems with both stabilizing and instabilizing impulses, systems with varying impulsive gain, and systems with Zeno behavior. Numerical simulations of stabilization for different systems by delayed saturated hybrid control have been conducted, which demonstrate the validity of proposed theorems.

A document by Zina Hakim. Click on the document to view its contents.

PULMONARY TUBERCULOSIS IN AN ADULT PRESENTING WITH SEVERE HYPONATREMIA: A CASE REPORT AND REVIEW OF LITERATUREShailes Paudel1,2 , Maulik Dhanani  2,3, Krish Hitesh Patel2,4, Naga Praneeth Vakkalagadda2,5, Vivek Sanker2,6, Abhiram Rao Damera2,7, Umang Gupta2,8, Prakriti Bhandari1,21Patan Academy of Health Sciences, Lalitpur, Nepal2 Team Erevnites, India3Southwestern University School of Medicine, Cebu City, Philippines4Government Medical College, Surat, India5Guntur Medical College, Andhra Pradesh, India6Noorul Islam Institute of Medical Sciences, Kerala, India7Mediciti Institute of Medical Sciences, Ghanpur, Telangana, India8 Nepalgunj medical college, BP Chowk-12, Nepalgunj Banke, Nepal

Introduction: Atypical fibroxanthoma is a low grade soft tissue sarcoma infiltrating the dermis containing pleomorphic spindle cells. Pleomorphic dermal sarcoma is a rare dermal tumour on the same morphological spectrum as atypical fibroxanthoma, with greater potential for malignancy. Distinguishing between the two tumours can have a significant impact on management. Case: An 81-year-old gentleman presents with a black haemorrhagic lesion demonstrating features of atypical fibroxanthoma. The diagnostic pathway of this case is followed, focusing on wide local excision and histopathological examination of the offensive tissue, and highlighting decisions made during these stages. Discussion: Common clinical features of atypical fibroxanthoma is detailed, and potential for larger trials to focus on clinical features in symptomatic individuals. The difficulty orientating auricular tissue and methods to reduce potential loss of tissue is discussed. Finally, this report delves into guidelines for wide local excision and what is appropriate for atypical fibroxanthoma or pleomorphic dermal sarcomas. Conclusion: Management of atypical fibroxanthoma can vary from pleomorphic dermal sarcoma or more aggressive sarcomas. This report emphasises the need for a streamlined diagnostic pathway to improve treatment benefit for patients and reduce waiting times.

Background: Langerhans cell histiocytosis (LCH) is the most common benign tumor in children, however its etiology remains incompletely understood. This aim of this study was to employ bioinformatics methods to identify differentially expressed genes (DEGs) in LCH patients. The identification of these DEGs can offer novel insights and research directions for the prevention, early diagnosis, and treatment of LCH. Methods: Gene expression data from GSE122476 was retrieved from the Gene Expression Omnibus (GEO) database. Using the R language, differentially expressed genes (DEGs) were identified between normal blood samples and bone tissue samples from LCH patients. The DEGs were subjected to functional and pathway analysis utilizing the DAVID database. The functional analysis, pathway enrichment, and protein-protein interactions of these genes were further examined using DAVID and STRING. To identify core network genes and significant protein interaction modules, Cytoscape software was employed. Results: A total of 229 DEGs were identified, comprising 107 up-regulated genes and 122 down-regulated genes. GO and KEGG enrichment analyses showed that the up-regulated DEGs were involved in processes such as immune response, hypoxia response, cell apoptosis, lipid antigen binding, and membrane composition. On the other hand, down-regulated DEGs were associated with inflammation, positive regulation of MAP kinase activity, and serine-type endopeptidase inhibitor activity. The KEGG enrichment analysis showed that the up-regulated DEGs exhibited significant enrichment in hematopoietic cell lineage, the interaction between viral proteins and cytokines and their receptors, TNF signaling pathway, cell apoptosis, and Th1, Th2, and Th17 cell differentiation. Conversely, the down-regulated DEGs were significantly enriched in cytokine receptor interaction, lysosome, and the PI3K-AKT signaling pathway. Further analysis using the STRING database resulted in the identification of 10 key genes within the protein network interaction map. These genes included TNF, CD69, IL7R, CCL4, IL3RA, CD1C, TNFRSF9, HAVCR2, IL2RG, and GZMA. Conclusion: In this study, 10 DEGs were identified as potential candidate diagnostic biomarkers for LCH patients. While these DEGs hold promise for their potential utility in LCH diagnosis, it is important to note that further experiments are required to validate their functional pathways and elucidate their roles as hub genes associated with LCH.

Objective: The purpose of this study was to examine the association between the implementation of a mobile obstetric emergency system (MORES) and referral time for obstetric emergencies as well as maternal/newborn outcomes. Design: Pre/post intervention descriptive study. Setting: Liberia’s caesarean section (CS) coverage is about 5%, lower than 9-19% coverage associated with decreased maternal mortality. Delays in the referral process from a rural health facility (RHF) to a district hospital for comprehensive emergency obstetric and newborn care (CEmONC) services such as CS contribute to the high maternal mortality. Sample: Women with referral data from both RHFs (n=20) and hospitals (n=2) within Bong County, Liberia. Methods: A pre/post descriptive analysis was conducted on data collected from RHFs and two hospitals in Bong County, Liberia. Descriptive analysis and logistic regression models examined the relationship between the intervention’s implementation and mode of delivery, maternal outcome, newborn outcome, and transfer time from RHF to district hospital. Main outcome measures: Mode of delivery, maternal outcome, newborn outcome, and transfer time. Results: Women had higher odds of undergoing a CS at endline (OR: 1.86 95%; CI: 0.99- 3.46) compared to baseline. Additionally, newborns had lower odds of showing depressive symptoms (OR: 0.31; 95%CI: 0.14-0.68), defined as a non-vigorous newborn with poor respiratory effort, muscle tone or heart rate <100 beats per minute. Conclusion: MORES is a promising means to increase timely care seeking along the referral pathway which may enhance access to caesarean section as well as improved newborn outcomes in low- and middle-income countries.

Objective This study was conducted to find out if the SSD Rhinoplasty induce less pain early post operatively which will plays an important role patients satisfaction and to evaluate the numbness after this relatively new procedure and numbness in an important and common side effect of the conventional Rhinoplasty. Methods One Rhinoplasty consultant has operated on 30 cases using SSD technique after informed and detailed consent, then the virtual pain score was recorded on the 7th day. And the numbness was evaluated 6 months after surgery. Results Patients in this series (18 men, 12 women) ranged in age from 23 to 54 years (mean, 24.3 years). 24 were primary Rhinoplasty, and 6 were revision Rhinoplasty; Pain scores: The study revealed that the median: 2.0 mean pain score median: 2.0. Numbness over the nose and nasal tip: 20 (83%) patients with primary Rhinoplasty had no numbness, but 4 (17%) patients had early numbness that improved after six months. However, all revision cases (6 patients) had short and long-term numbness after 6 months. Conclusions. SSD Rhinoplasty shortens the dissection time and makes it faster to raise the envelope even with revision cases. Also, it procured much less pain among all patients. In addition, it induces minimal numbness. However, this study used a small number of patients and did not consider the procedure done after raising the nasal envelope.

Introduction Osteonecrosis of bone, a complex process of bone resorption and formation, is characterized by necrosis of bone cells1. There are numerous etiologies and risk factors nominated for this situation. Vascular interruption, thrombotic occlusion, and extravascular compression lead to decreased blood flow, and the reason will be avascular necrosis (AVN) of bone1. Intracapsular or extracapsular hip fractures, hip dislocations, sickle cell anemia, coagulation factor anomalies, corticosteroids, alcohol, pregnancy, radiation, bone marrow transplant, Hyperlipidemia, hyperuricemia, pancreatitis, leukemia or lymphoma, hypertriglyceridemia, and dysbaric situation are the risk factors of AVN1–6. The incidence of AVN in the general population is uncertain and was not assessed well in risk groups7.Although osteonecrosis of the femoral head (ONFH) has been studied, the literature is so far sparse on osteonecrosis of the acetabulum (ONA). Here, we aim to report on a case of ONA.

This work proposes a multi-target tracking and detection algorithm Focus-MOT based on feature refinement extraction fusion, t through the designed Field Enhancement Refinement Module and Information Aggregation Module, which effectively reduces the number of target ID switching.Jointly learns the Detector and Embedding model method becomes the mainstream of multi-target tracking and detection due to its fast detection speed, its Re-ID branch needs to use low-dimensional features and high-dimensional features to accommodate both large and small targets, however, its insufficient feature extraction leads to high ID_SW. Therefore this work aims to extract features of different levels for aggregation as a way to reduce the number of ID switching. The experimental results show a 2.7% improvement in MOTA and a 2300 times decrease in ID_SW relative to the results of the FairMOT algorithm on the MOT17 dataset.

Background: Exposure to ambient particulate matter (PM) has been associated with an increased risk of allergic rhinitis in children. However, it is unclear whether food allergy modifies the association between PM exposure and childhood allergic rhinitis. Objectives: We aimed to evaluate the modification of food allergy on the association between PM exposure and allergic rhinitis in preschool children. Methods: We adopted a cross-sectional study and conducted a questionnaire survey among preschool children aged 3 to 6 years in 7 cities in China from June 2019 to June 2020 to collect information on allergic rhinitis. A mature machine learning-based space-time extremely randomized trees model was applied to estimate early-life, prenatal, and first-year exposure of PM 1, PM 2.5 and PM 10 at 1 × 1-km resolution. We used a combination of multilevel logistic regression and restricted cubic spline functions to quantitatively assess whether food allergy modifies the associations between size-specific PM exposure and the risk of childhood allergic rhinitis. Results: The adjusted ORs for childhood allergic rhinitis among the children with food allergy as per interquartile range (IQR) increase in early-life PM 1, PM 2.5 and PM 10 were significantly higher than the corresponding ORs among the children without food allergy [e.g. OR: 1.57, 95% CI (1.32, 1.87) vs. 1.29, 95% CI (1.18, 1.41), for per IQR increase in PM 1 (9.8 μg/m 3)]. The similar patterns were observed for both prenatal and first-year size-specific PM exposure. The interactions between food allergy and size-specific PM exposure on childhood allergic rhinitis were statistically significant (all p- int < 0.001). Conclusions: Food allergy, as an important part of the allergic disease progression, may modify the association between ambient PM exposure and the risk of childhood allergic rhinitis. Children with food allergy should pay more attention to minimize outdoor air pollutants exposure to prevent the further progression of allergic diseases.

The use of artificial intelligence (AI) and machine learning (ML) has significantly enhanced ecological research in Asia by improving data processing, analysis, and pattern extraction. Analyzing 1550 articles, I show an overview of the use of AI and ML for Asian ecological research. Following the last 20 year trend, I found that the topics in Asian ecological research have transitioned from technical perspectives to more applied issues, focusing on biodiversity conservation, climate change, land use change, and societal impacts. Non-Asian countries, on the other hand, have focused more on theoretical understanding and ecological processes. The difference between Asian and non-Asian regions may have emerged due to the ecological challenges faced by Asian countries, such as rapid economic growth, land development, and climate change impacts. In both regions, deep learning related technology has been emerging (e.g. big data collection including image and movement). Within Asia, China has been the Asia-leading country for AI/ML applications followed by Korea, Japan, India, and Iran. The number of computer science education programs for education in China has been increasing 3.5x times faster than that in the U.S., indicating that a nationwide strategy for computer science development is key for ecological science with AI. Overall, the adoption of AI and ML technologies in ecological studies in Asia has propelled the field forward and opened new avenues for innovative research and conservation practices.