Large Number Research Articles

Nanotechnology-Based Drug Delivery of Topical Antifungal Agents.

Among the various prominent fungal infections, superficial ones are widespread. A large number of antifungal agents and their formulations for topical use are commercially available. They have some pharmacokinetic limitations which cannot be retracted by conventional delivery systems. While nanoformulations composed of lipidic and polymeric nanoparticles have the potential to overcome the limitations of conventional systems. The broad spectrum category of antifungals i.e. azoles (ketoconazole, voriconazole, econazole, miconazole, etc.) nanoparticles have been designed, prepared and their pharmacokinetic and pharmacodynamic profile was established. This review briefly elaborates on the types of nano-based topical drug delivery systems and portrays their advantages for researchers in the related field to benefit the available antifungal therapeutics.

District energy modelling for decarbonisation strategies development—The case of a University campus

University campuses can be considered as microcosms of urban districts, since they are typically located within or near a city and have a significant impact on the surrounding area. Moreover, they have a high energy consumption due to the large number of buildings, facilities, and services that use energy for transportation, heating, cooling, and lighting. This makes university campus as testing facility for decarbonisation strategies that can be replicated in other urban areas. The present study discusses a decision-making framework based on a digital twin model for analysing decarbonisation strategies of University College Dublin in Ireland. The thermal and electricity networks of the university campus are modelled to analyse the current status of energy consumption and associated carbon dioxide emissions. Five future development scenarios are explored to identify the optimum pathway for decarbonisation. Conclusions show that the campus can achieve a 10% reduction in emissions without the need for new generation units, while introduction of a new biomass CHP unit would increase this reduction to 17% compared to the baseline. Furthermore, Coupling this with the installation of 3 MW of solar photovoltaic, the calculated campus potential, a total reduction of 26% can be achieved.

Computational protein design - where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

Traditional Therapies Involving Nutrition for the Management of COVID-19

Abstract: The novel coronavirus SARS-CoV-2 causes ARDS, respiratory failure, organ failure, and even death. The immunopathology associated with the SARS-CoV-2 virus involves an overproduction of pro-inflammatory cytokines, commonly referred to as a "cytokine storm." SARS-CoV-2 is a positive-sense RNA virus responsible for the COVID-19 pandemic. The pandemic kept the world silent since its emergence by engulfing most public activities. The symptoms of COVID-19 like coughing, fatigue, dysphagia, and poor appetite reduce the nutritional intake among the patients. Therefore, the nutritional status of the infected patients needs monitoring. The deficiency of nutrition puts the patients at high risk during clinical therapy. Moreover, insufficient micronutrients lead to weakened immune responses, causing improper cytokine secretion, changes in secretory antibody response, and reduced antibody affinity; all of which enhance the vulnerability of viral infections. As the pandemic is affecting a large number of people all over the world, there is an urgent requirement for newer therapies/therapeutics with nutritional information for hospitalized and recovered patients of COVID-19. Hospitalized patients are highly at risk of malnutrition, weight loss, muscle loss, etc. Therefore, nutritional support is essential for COVID-19 patients. Based on the recent reports obtained from the COVID-19 resources; the present review describes the various clinical therapies, the involvement of nutritional support, the role of diet, the modified lifestyle, and micronutrients (zinc, selenium, and vitamin D) in the management of COVID-19 patients.

Pott's puffy tumor: An unusual complication of rhino-orbito-cerebral mucormycosis

ObjectiveTo describe clinicoradiological features and surgical outcomes in a series of nine patients with rhino-orbito-cerebral mucormycosis (ROCM) who presented with Pott's puffy tumor (ROCM-PPT) MethodsThe records of nine patients with ROCM-PPT seen between March 2020 and December 2021 were analysed. Clinical features, radiology, histopathology, operative findings, management and outcome were noted. Frontal sinus pneumatisation and outflow tract configuration was compared between patients and controls with ROCM and no PPT. ResultsROCM-PPT was diagnosed in 9 of 284 (3.2 %) patients with ROCM seen during the study period. There were six (66.7 %) males and the median age was 54 (IQR 46–60) years. Eight (88.9 %) patients had diabetes mellitus and seven (77.8 %) had been COVID-19 positive. Radiological features of osteomyelitis, subperiosteal abscess formation and dural enhancement were seen in all patients. No significant differences in pneumatisation or frontal sinus outflow tract configuration were noted between patients and controls. All patients underwent a craniectomy with frontal bone debridement and frontal sinus exteriorisation. All patients were treated with anti-fungal agents for several months. All patients had symptomatic improvement at a median follow-up of 21 (IQR 18–23) months. Repeat CT/MRI scans showed disease regression/resolution in six out of eight (75 %) patients with follow-up imaging, and stable disease in two others. ConclusionsROCM-PPT is a rare, delayed complication of mucormycosis that was seen in larger numbers during the recent COVID-19 pandemic. Aggressive debridement of osteomyelitic bone and antifungal therapy results in a good outcome.

Abstract PO5-11-11: Risk Assessment, Prevention and Early Detection of Breast Cancer Related Lymphedema – Objective Measurements and Patient Reported Outcomes

Abstract Background Breast cancer-related lymphedema (BCRL) affects the quality of life, but there is no consensus regarding early detection and monitoring. Patient-reported outcomes (PROs) are important in assessing cancer survivor outcomes, and different questionnaires have been developed. LYMPHA (Lymphatic Microsurgical Preventive Healing Approach) and S-LYMPHA (Simplified LYMPHA) have reduced BCRL rates. This study aims to identify the most reliable PRO in a South Florida multi-ethnic population and to study correlations between PROs and objective measurements in the first 6 months after axillary surgery. Methods Patients undergoing axillary lymph node dissection (ALND) or axillary radiation were included. L-Dex score (Bioimpedance Spectroscopy) and three validated questionnaires (LyQLI, Lymphedema Quality of Life Inventory; LyQOL, Lymphedema Quality of Life and FACT-B4+ Functional Assessment of Cancer Therapy – Lymphedema) were recorded at baseline, and 6 months post-surgery to assess the agreement between patient-reported symptoms and BCRL. L-Dex score outside the normal range or a 10 unit increase above baseline was considered lymphedema. Additional variables such as demographics, tumor characteristics, and treatment modalities were recorded. Results Out of 40 recruited patients, 39 were analyzed (excluding one deceased patient). Patient and treatment characteristics are described in Table 1. Patients were divided into three cohorts (Figure 1) for analysis: ALND with no axillary radiation (7.69%; n=33), Sentinel Lymph Node Biopsy (SLNB) with axillary radiation (7.69 %; n=3), and ALND with axillary radiation (85%; n=3). In the first cohort, LYMPHA or S-LYMPHA were associated with lower rates of lymphedema (19% vs, 50%) (p=0.116). No patients in the second or third cohort developed lymphedema. There were 4 out of 20 (20%) patients with lymphedema with scores above the median for FACT B4+ (p= 0.480) and a lower score (higher quality of life) correlated with lower L-Dex (p=0.76). The higher value in the physical domain of LyQLI showed higher L-Dex (p=0.826). There was a significant inter-domain correlation in LyQLI and LyQOL (p= < 0.001). Conclusion These results validate the use of PROs alongside objective measurements to assess BCRL. In addition, LYMPHA and S-LYMPHA significantly reduce lymphedema rates. Larger numbers will be necessary to reach statistical significance and to identify the best PRO. Figure 1 Division of patient population into three cohorts Table 1 Demographic and Treatment Summary Citation Format: Anshumi Desai, Orli-Friedman Eldar, Gili Halfteck, Mecker Moller, Susan Kesmodel, Dido Franceschi, Neha Goel, Jessica Crystal, Laura Huang, Jennifer Hu, Koru-Sengal Tulay, Wei Zhao, Alexis Narvaez-Rojas, Eli Avisar. Risk Assessment, Prevention and Early Detection of Breast Cancer Related Lymphedema – Objective Measurements and Patient Reported Outcomes [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-11-11.

Primary arthrodesis for diabetic ankle fractures using a modified retrograde femoral intramedullary locking nail combined with lateral plating, surgical technique, and early results of a pilot study.

We aimed to report the early results of performing acute ankle arthrodesis using a modified retrograde femoral intramedullary locking IMN concomitant with plating at the same setting for managing diabetic patients' acute ankle fractures. We prospectively included patients who presented acutely with ankle fractures, where hemoglobin A1C (HbA1C) on admission was > 7%, and the Adelaide Fracture in the Diabetic Ankle (AFDA) algorithm score was 5 or above. All patients were treated by acute ankle arthrodesis using a modified retrograde femoral IMN combined with lateral plating. Functional assessment was reported according to a modified American Orthopaedic Foot and Ankle Society ankle hindfoot scale (AOFAS), and complications were documented. Six patients had an average age of 55.7years (37-65). The average HbA1C on admission was 7.9 (7.3-9), and the average AFDA score was 7.3 (6-8). The average operative time was 79.2min (70-90). All patients, except for one, achieved union at the arthrodesis site after an average of 10.3weeks (8-14). After an average last follow-up of 9months (6-12), the average modified AOFAS was 73.2 (82 to 62); four patients had an excellent score and one good. Complications developed in two, one deep infection after 2weeks treated by metal removal and Ilizarov, and the other patient developed a stress fracture at the tibial end of the nail, which was treated by open reduction and internal fixation using a plate and screws. Using a modified femoral IMN combined with lateral plating is a promising technique to achieve ankle arthrodesis in diabetic patients with acute ankle fractures with acceptable outcomes; however, further studies with larger numbers are needed. IV.

CRISPR/Cas9 Technology for Non-Coding Gene Editing in Schizophrenia Therapeutics: The Recent Progress And Challenges

Abstract: Within a decade the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR/Cas9 system), an advanced gene-editing technology became one of the celebrated approaches in modern disease therapeutics and was reported to have a potential role in the alteration of non-coding RNAs segment which are the pivotal causes behind the several mental disorder such as Schizophrenia. In general, Schizophrenia is referred as a neurodevelopmental disorder and symptomatically exhibited by social deficit, cognitive dysfunction, apathy, delusions, hallucinations, etc. At a genomics level large number of loci are susceptible for genetic alteration in schizophrenia and are mostly located in the genome’s non-coding region. With the growing variants and mutations in ncRNA genes (miRNA and lncRNA) strongly associated with schizophrenia, the need to develop a genetic tool to help with the treatment and study of schizophrenia increases. Recently the use of CRISPR/cas9 technology in the productive alteration of non-coding RNAs genes such as miRNA; miR-291, miR-141, and miR-21, lncRNA, lncRNA-21A, AK023948, and LncRNA Rian has been reported. The Cas9 protein and guide RNA (gRNA) together form the CRISPR/Cas9 system is known to be highly specific and efficient for manipulating the impact of gene mutations linked to genomic DNA like ncRNA besides other inheritable genetic diseases. Copy number variations are also found to be linked with schizophrenia. The generation of reciprocal CNVs of 15q13.3 and 16p11.2 in humaninduced pluripotent stem cells (iPSCs) with the CRISPR/Cas 9 system has opened new possibilities. Still, there are some limitations and challenges yet to be defeated, like the blood-brain barrier poses an obstacle to treating mental disorders and ethical issues like genomic DNA manipulation of eggs and embryos. This review brings schizophrenia-associated ncRNAs and CRISPR gene-editing technology for the non-coding parts of the genomic DNA together and recent challenges.

Molecular Aspects of Carrageenan in the Pharmaceutical and Food Industries

Abstract: Carrageenan is a gel-forming polysaccharide obtained by extracting certain species of red algae. This substance is used in several food groups due to carrageenan's ability to replace fat and combine easily with milk proteins to increase solubility and improve texture. Although the FDA (Food and Drug Administration) considers carrageenan safe for consumption, this point is still subject to intense scrutiny to investigate the potential health risks associated with human consumption. In view of this, the objective of this review was to address the consumption of carrageenan in the food and pharmaceutical industries and its possible toxic effects on health. One hundred and eleven articles were related to the objective of this review. Although carrageenan is widely used for consumption and several studies describe its beneficial effects, a large number of articles have already been bringing possible toxic effects that it can generate in the long term.

Review of Patents Analysis on Floor Robot Vacuum

Background: As the pace of life gets faster, and with the development and progress of society and the continuous improvement of people's pursuit of a convenient and comfortable life, it has become a growing desire for people to get rid of tedious housework. Hygienic automatic cleaning has also become the main demand of people. Objective: Through review, analysis, and discussion of the patents on existing floor robot vacuums, it is expected to find out the development process and main outcomes, along with the main technologies involved, and provide a valuable prediction basis for future research and development directions. Methods: This study aimed to summarize a number of representative patents related to floor robot vacuums and their main components and analyze them in depth to study their technical points and development laws. Results: By summarizing a large number of patents on floor robot vacuums and further analyzing and comparing the characteristics of the patents, the future development trend of floor robot vacuums was further discussed. Conclusion: Through a detailed analysis of the mechanical structure, intelligent control technology, and sensor technology of the existing floor robot vacuums as a whole and the main work unit, this study discussed the ideas and methods of improving the new floor robot vacuum, which is of great significance to its operation and more convenient and intelligent.

The HPA Axis as Target for Depression.

Major depressive disorder (MDD) is a stress-related mental disorder with a lifetime prevalence of 20% and, thus, is one of the most prevalent mental health disorders worldwide. Many studies with a large number of patients support the notion that abnormalities of the hypothalamus-pituitaryadrenal (HPA) axis are crucial for the development of MDD. Therefore, a number of strategies and drugs have been investigated to target different components of the HPA axis: 1) corticotrophinreleasing hormone (CRH) 1 receptor antagonists; 2) vasopressin V1B receptor antagonists, 3) glucocorticoid receptor antagonists, and 4) FKBP5 antagonists. Until now, V1B receptor antagonists and GR antagonists have provided the most promising results. Preclinical data also support antagonists of FKBP5, which seem to be partly responsible for the effects exerted by ketamine. However, as HPA axis alterations occur only in a subset of patients, specific treatment approaches that target only single components of the HPA axis will be effective only in this subset of patients. Companion tests that measure the function of the HPA axis and identify patients with an impaired HPA axis, such as the dexamethasone-corticotrophin-releasing hormone (dex-CRH) test or the molecular dexamethasonesuppression (mDST) test, may match the patient with an effective treatment to enable patient-tailored treatments in terms of a precision medicine approach.

Semi-Supervised Multiscale Dynamic Graph Convolution Network for Hyperspectral Image Classification.

In recent years, convolutional neural networks (CNNs)-based methods achieve cracking performance on hyperspectral image (HSI) classification tasks, due to its hierarchical structure and strong nonlinear fitting capacity. Most of them, however, are supervised approaches that need a large number of labeled data to train them. Conventional convolution kernels are fixed shape of rectangular with fixed sizes, which are good at capturing short-range relations between pixels within HSIs but ignore the long-range context within HSIs, limiting their performance. To overcome the limitations mentioned above, we present a dynamic multiscale graph convolutional network (GCN) classifier (DMSGer). DMSGer first constructs a relatively small graph at region-level based on a superpixel segmentation algorithm and metric-learning. A dynamic pixel-level feature update strategy is then applied to the region-level adjacency matrix, which can help DMSGer learn the pixel representation dynamically. Finally, to deeply understand the complex contents within HSIs, our model is expanded into a multiscale version. On the one hand, by introducing graph learning theory, DMSGer accomplishes HSI classification tasks in a semi-supervised manner, relieving the pressure of collecting abundant labeled samples. Superpixels are generally in irregular shapes and sizes which can group only similar pixels in a neighborhood. On the other hand, based on the proposed dynamic-GCN, the pixel-level and region-level information can be captured simultaneously in one graph convolution layer such that the classification results can be improved. Also, due to the proper multiscale expansion, more helpful information can be captured from HSIs. Extensive experiments were conducted on four public HSIs, and the promising results illustrate that our DMSGer is robust in classifying HSIs. Our source codes are available at https://github.com/TangXu-Group/DMSGer.

Main Aspects of Pharmaceutical Development of In situ Immunobiological Drugs for Intranasal Administration.

The review presents the latest developments in the area of intranasal in situ delivery systems of immunobiological drugs (IBDs). Interest in intranasal administration for IBDs has increased significantly due to the COVID-19 pandemic. However, not only intranasal delivery of vaccines is developing, but also bacteriophages, interferons, etc. In situ systems that make a selective phase transition can be a modern solution to intranasal delivery problems caused by mucociliary clearance. In addition, smart-polymers used as the main excipients in in situ systems can be used as specific adjuvants. A scientific search was conducted on the PubMed database of medical publications for the period from 2000 to 2022, using the keywords "intranasal in situ vaccine"; "intranasal in situ immunization". There were analyzed in detail more than 70 scientific studies on intranasal in situ delivery of IBDs. Despite the large number of new studies, the potential of possibilities of intranasal in situ systems is not being realized. Based on the results of the literature review an algorithm was created for the development of in situ systems for intranasal delivery of IBDs. Such algorithms and the methods of study design organization described in the review will help to facilitate the R&D process and bring the drug to commercial market, which will help to improve the quality of medical care.

Evolution of coronal mass ejections with and without sheaths from the inner to the outer heliosphere: Statistical investigation for 1975 to 2022

Aims. This study covers a thorough statistical investigation of the evolution of interplanetary coronal mass ejections (ICMEs) with and without sheaths through a broad heliocentric distance and temporal range. The analysis treats the sheath and magnetic obstacle (MO) separately in order to gain more insight on their physical properties. In detail, we aim to unravel different characteristics of these structures occurring over the inner and outer heliosphere. Methods. The method is based on a large statistical sample of ICMEs probed over different distances in the heliosphere. For this, information about detection times for the sheath and MO from 13 individual ICME catalogs was collected and crosschecked. The time information was then combined into a main catalog that was used as the basis for the statistical investigation. The data analysis based on this catalog covers a large number of spacecraft missions, enabling in situ solar wind measurements from 1975 to 2022. This allowed us to study the differences between solar cycles. Results. All the structures under study (sheath, MO with and without sheath) show the biggest increase in size together with the largest decrease in density at a distance of ∼0.75 AU. At 1 AU, we found different sizes for MOs with and without a sheath, with the former being larger. Up to 1 AU, the upstream solar wind shows the strongest pileup close to the interface with the sheath. For larger distances, the pileup region seems to shift, and it recedes from that interface further into the upstream solar wind. This might refer to a change in the sheath formation mechanism (driven versus non-driven) with heliocentric distance, suggesting the relevance of the CME propagation and the expansion behavior in the outer heliosphere. A comparison to previous studies showed inconsistencies over the solar cycle, which makes more detailed studies necessary in order to fully understand the evolution of ICME structures.

The Organizational Structure, Conduct, and Outcomes of Coimbatore and Erode District Companies Using Different Sugar Substitutes

According to a study on market performance, producers will still receive a higher percentage of the profit even if they sell on a larger scale than other actors. The price difference for ten kilograms of sugar was calculated to be Rs. 100. Still, a lot of consumers choose to pay more for sugar in retail establishments when they buy it in smaller amounts rather than in bulk. Even if the price difference for ten kilograms of sugar is only Rs. 90, the producer is making a sizable profit margin, according to a market performance analysis. Simple percentage and averages were worked out to assess the general characteristics such as age and experience of the intermediaries and consumers. However, in this instance, the producer sells in larger numbers, either through the market or directly to the wholesaler, enabling them to keep a considerable portion of the earnings from their products. A market performance analysis indicates that the producer will share in the profit margin at a higher rate than other chain participants. Because 20 kilograms of palm sugar can be made from 60 liters of palmyrah tree sap, his production expenses will match farmers profit margin.

Improving Semi-Supervised Text Classification with Dual Meta-Learning

The goal of semi-supervised text classification (SSTC) is to train a model by exploring both a small number of labeled data and a large number of unlabeled data, such that the learned semi-supervised classifier performs better than the supervised classifier trained on solely the labeled samples. Pseudo-labeling is one of the most widely used SSTC techniques, which trains a teacher classifier with a small number of labeled examples to predict pseudo labels for the unlabeled data. The generated pseudo-labeled examples are then utilized to train a student classifier, such that the learned student classifier can outperform the teacher classifier. Nevertheless, the predicted pseudo labels may be inaccurate, making the performance of the student classifier degraded. The student classifier may perform even worse than the teacher classifier. To alleviate this issue, in this paper, we introduce a dual meta-learning ( DML ) technique for semi-supervised text classification, which improves the teacher and student classifiers simultaneously in an iterative manner. Specifically, we propose a meta-noise correction method to improve the student classifier by proposing a Noise Transition Matrix (NTM) with meta-learning to rectify the noisy pseudo labels. In addition, we devise a meta pseudo supervision method to improve the teacher classifier. Concretely, we exploit the feedback performance from the student classifier to further guide the teacher classifier to produce more accurate pseudo labels for the unlabeled data. In this way, both teacher and student classifiers can co-evolve in the iterative training process. Extensive experiments on four benchmark datasets highlight the effectiveness of our DML method against existing state-of-the-art methods for semi-supervised text classification. We release our code and data of this paper publicly at https://github.com/GRIT621/DML.

Consumer Perceptions of the Canadian Salmon Sector and Their Associations with Behaviors: A Perspective from Indigenous Rights.

Previous studies on consumer perceptions and behaviors of salmon have often neglected Indigenous rights within the Canadian salmon sector. This study innovatively addresses this gap by integrating Indigenous rights into the current analysis, alongside considerations of sustainability practices, socio-economic impacts, and consumer motivations. Our research objectives aim to fit three consumer perceptions-environmental sustainability, economic considerations, and Indigenous rights-and to evaluate their associations, alongside perception of a price increase, socio-demographics, and consumer motivation factors, with purchasing behaviors related to Canadian salmon products. Data for this study was collected from a nationwide online survey. Responses to Question 2 and Question 35 are encoded with numerical values ranging from 1 to 5, where larger numbers indicate stronger agreement with the statement. The inclusion of methodologies such as the Graded Response Model (GRM) and Cumulative Link Models (CLM) adds another innovative dimension to this study. Our findings demonstrate how consumer profiles are associated with these four perceptions and their underlying determinants. Furthermore, the study quantifies the influence of these four perceptions on each consumer purchase behavior. The implications of these findings extend to the realm of mathematical modeling in consumer decision-making processes, offering practical insights for businesses and marketers, and emphasizing the importance of implementing regulatory frameworks and initiatives that promote sustainability, safeguard Indigenous rights, and address socio-economic disparities.

Application of depth feature recognition technology in foreign object recognition in distribution network monitoring video

Foreign objects identification in the distribution network is an important link in the security of electric power, and is of great significance to the normal transportation of electric power. At present, a lot of equipment in the distribution network is in the open air environment, facing a large number of foreign interference. These foreign objects not only bring potential safety hazards to the distribution network, but also easily lead to short circuit, causing power supply difficulties within the region. Therefore, the research first constructs an optimized triplet feature learning model. On this basis, the HOG-SVM depth feature recognition model is proposed. In HOG-SVM, AM is introduced to improve recognition accuracy. In addition, the research enhances the night vision ability of the model by standardizing the features in the image region block. The results show that the AP of the model is stable at more than 90.54%, the average FPR is 2.21%, and the average FNR is 3.17%. The performance of HOG-SVM is significantly better than that of traditional SVM. It verifies the contribution of this research in the field of foreign object recognition and application value in ensuring the security of distribution network.

Demand side management using ant colony optimization algorithm in renewable energy integrated smart grid

The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous for the power industry’s pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer’s cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context.

Combining permuted language model and adversarial training for Chinese machine reading comprehension

Due to the polysemy and complexity of the Chinese language, Chinese machine reading comprehension has always been a challenging task. To improve the semantic understanding and robustness of Chinese machine reading comprehension models, we propose a model that utilizes adversarial training algorithms and Permuted Language Model (PERT). Firstly, we employ the PERT pre-training model to embed paragraphs and questions into vector space to obtain corresponding sequential representations. Secondly, we use a multi-head self-attention mechanism to extract key textual information from the sequence and employ a Bi-GRU network to semantically fuse the output feature vectors, aiming to learn deep semantic representations in the text. Finally, we introduce perturbations into the model training process. We achieve this by utilizing adversarial training algorithms such as Fast Gradient Method (FGM) and Projected Gradient Descent (PGD). These algorithms generate adversarial samples to enhance the model’s robustness and stability when facing diverse inputs. We conducted comparative experiments on the publicly available Chinese reading comprehension datasets CMRC2018 and DRCD. The experimental results show that our proposed model has achieved significant improvements in both EM and F1-Score compared to the baseline model. To validate the model’s generalization and robustness, we utilized ChatGPT to construct a scientific dataset that includes a large number of domain-specific terms, sentences with mixed Chinese and English, and complex comprehension tasks. Our model also performed remarkably well on the self-built dataset. In conclusion, the proposed model not only effectively enhances the understanding of semantic information in Chinese text but also demonstrates a certain level of generalization capability.