General Features Research Articles

A Novel Federated Learning Scheme for Generative Adversarial Networks

Generative adversarial networks (GANs) have been advancing and gaining tremendous interests from both academia and industry. With the development of wireless technologies, a huge amount of data generated at the network edge provides an unprecedented opportunity to develop GANs applications. However, due to the constraints such as bandwidth, privacy, and legal issues, it is inappropriate to collect and send all data to the cloud or servers for analysis, training, and mining. Thus, deploying and training GANs at the edge becomes a promising alternative solution. The instability of GANs introduced by non-independent and identical data (Non-IID) poses significant challenges to training GANs. To address these challenges, this paper presents a novel federated learning framework for GANs, namely, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ollaborated g <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> me <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> arallel Learning (CAP). CAP supports parallel training of data and models for GANs, breaking the isolated training among generators that exists in the previous distributed algorithms, and achieving collaborative learning among cloud, edge servers, and devices. Then, to further enhance the ability of CAP-GAN for addressing Non-IID issues, we propose a Mix-Generator module (Mix-G) which divides a generator into the sharing layer and personalizing layer. The Mix-G module extracts the generic and personalization features and improves the performance of CAP-GAN on extremely personalizing datasets. Experimental results and analysis substantiate the usefulness and superiority of our proposed CAP-GAN scheme which can achieve better results in the Non-IID scenarios compared with the state-of-the-art algorithms.

Clinical data analysis of 117 patients with ruptured abdominal aortic aneurysm

Objective: To examine the perioperative clinical features and prognosis of patients with ruptured abdominal aortic aneurysms (rAAA) who received surgical repair. Methods: The clinical data of rAAA patients who underwent surgical repair and were admitted to the Surgical Intensive Care Unit of Beijing Anzhen Hospital, Capital Medical University from August 2005 to November 2020 were retrospectively analyzed, including the general clinical features, surgical mode, intraoperative conditions, postoperative complications, and fatality rate. Results: There were 117 patients with rAAA, with a median age of 68 (62,77) years, including 93 men (79.5%) and 24 women (20.5%). The main clinical manifestation was abdominal pain (n=115, 98.3%). Among them, 65 (55.6%) patients underwent endovascular aneurysm repair (EVAR), while 52 (44.4%) underwent open surgical repair (OSR). The common postoperative complications include acute gastrointestinal dysfunction (n=116, 99.1%), shock (n=89, 76.1%), acute respiratory distress syndrome (n=85, 72.6%), pancreatic injury (n=56, 47.9%), coagulation dysfunction (n=55, 47.0%), disseminated intravascular coagulation (n=46, 39.3%), acute kidney injury (n=39, 33.3%), infection/sepsis (n=28, 23.9%), gastrointestinal bleeding (n=17, 14.5%), and abdominal compartment syndrome (n=12, 10.3%). The overall postoperative in-hospital fatality rate was 10.3% (12/117). Preoperative use of vasopressors and inotropes, retroperitoneal hematoma, and postoperative abdominal compartment syndrome, gastrointestinal hemorrhage, acute kidney injury, and diffuse intravascular coagulation significantly increased the fatality rate [5/11, 6/24, 5/16, 6/12, 6/17, 23.1%(9/39), 19.6%(9/46), respectively]. Conclusion: The postoperative mortality of rAAA patients is still high in the era of EVAR, especially in patients with preoperative existence of shock and retroperitoneal hematoma, and with postoperative abdominal compartment syndrome, coagulation dysfunction, and acute kidney injury. It is necessary to strengthen perioperative monitoring and management of these patients to reduce the fatality rate.

FEATURES OF THE DAILY BLOOD PRESSURE PROFILE IN PATIENTS WITH ARTERIAL HYPERTENSION AND CHRONIC OBSTRUCTIVE PULMONARY DISEASE

Objective: To study the features of the daily blood pressure profile (DBPP) in patients with combination of arterial hypertension (AH) and chronic obstructive pulmonary disease (COPD). Design and method: The study included 47 patients (average age 53.1±2.5 years), 31 women, 16 men with hypertension I-II degrees. Patients were divided into 2 groups: with COPD (n=22) and without COPD (n=25). Duration AH in the group of patients with COPD was 6.5±0.54 years, without COPD 5.1±0.66 years. Exclusion criteria's were: symptomatic hypertension, cerebrovascular accidents in history, valvular heart disease, severe rhythm and conduction disorders, endocrine disorders, severe pathologies of the liver and kidneys, receiving systemic glucocorticosteroids actions. All patients underwent a study of pulmonary function (RPF) and SMAD recorders. Applied the following patient classification scheme: “dippers” (Daily index (DI) from 10 to 20%); “nondippers” (DI from 0 to 10%); “overdippers” (DI&gt;20%); “nightpickers” (DI&lt;0). Results: We identified a direct correlation between the level of PBP and age patients with a combination of hypertension and COPD during all monitoring periods (r=0.629, p=0.000 (days), r=0.645, p=0.000 (day), r=0.564, p=0.000 (night)). The same dependence was noted in individuals with hypertension without lung pathology (r=0.298, p=0.021 (days), r=0.266, p=0.04 (day), r=0.346, p=0.007 (night)). Thus, we not only did not identify significant differences in the level of PBP, but also noted general regular features of its dynamics with increasing age of patients of both groups. Comparative assessment of the heart rhythm. DBP also showed significant intergroup differences. Thus, we have established the predominance of “dippers” among patients with AH without lung pathology (51.9%), while among persons with a combination of hypertension and COPD, adequate nocturnal decrease in blood pressure was observed slightly more than in a third of patients (37.4%, p=0.125). Besides, among these patients a significantly higher number of “night pickers” was identified (25% vs. 7.5%, p=0.002), the number of “nondippers” and there were “overdippers” in the compared groups was approximately the same. Conclusions: Presence of COPD in patients with hypertension worsens the course of the latter, which is expressed in a significant predominance of “nightpickers” and “non-dippers” patients.

Анализ современных российско-китайских отношений в торгово-экономической сфере: особенности и перспективы развития

Currently, in the modern globalized world, a rather difficult situation is emerging. In geopolitical terms, it can be characterized by the development of the process of transitioning from a unipolar (American-centric) world to a multipolar one. More and more countries in the world are not just declaring the concept of state sovereignty but are also making attempts to actually implement it, both in their own development and within the whole world. The Russian Federation acted as the locomotive for the development of this global process. In geoeconomic terms, the response of Western countries was the introduction of unprecedentedly harsh sanctions against Russia and the announcement of various threats to other subjects of the world economy that do not adhere to the Western position from their point of view. First of all, we should talk about China, which openly declared the strengthening of its state sovereignty. In this regard, the vector of rotation in various areas of Russia’s international cooperation from West to East is fully justified. The article examines modern Russian-Chinese relations in the trade and economic spheres. The dynamics of the main macroeconomic indicators of the countries under consideration are presented. The main indicators and features of the foreign trade interaction between Russia and China are analyzed. Potential directions for the development of Russian-Chinese trade and economic relations are determined. A conclusion is drawn about the general features of the development of Russian-Chinese relations in the trade and economic sphere within the framework of the current modern global geopolitical situation.

Crop Classification with Attention Based BI-LSTM and Temporal Convolution Neural Network Combination for Remote Sensing Breizhcrop Time Series Data

In the modern era, remote sensing data has become increasingly useful for determining land use and coverage requirements. Remote sensing data can be used for a variety of purposes, including the classification of crops. It is possible to aggregate remote sensing data for a specific area over time in order to obtain a more complete picture based on the time series of this data. One example of these types of data is the Breizhcrop dataset, which was collected using satellite images acquired by Sentinel 2 over a period of time. This study aims to investigate a neural network based on attention mechanisms using the BI-LSTM layer in conjunction with Temporal-CNN for the classification of crops. The aim of the research is to find a model for corps classification in image-based time series. In line with this goal, in addition to finding features over time, the presented model also needs to produce high-accuracy features at each time step to increase classification. Utilizing the designed neural network, we seek to find local features with the attention mechanism and general features with a second layer. This neural network was validated on the BreizhCrop dataset and we conclude that it performs better than alternative approaches. The proposed method has been compared with Temporal CNN, Star RNN, and Vanilla LSTM networks and it has obtained better results than the mentioned neural networks. Taking advantage of these local and global features that extract with developed model obtained a high accuracy rate of 82%.

Meta-learning of feature distribution alignment for enhanced feature sharing

Meta-learning aims to learn common feature representations between tasks to quickly adapt and perform well when facing new tasks. However, current meta-learning algorithms ignore the differences in feature distributions among tasks. The significant differences in task features may lead to an over-fitting of the model to specific features of certain tasks during the learning process, thereby causing a decrease in adaptability to other tasks. In this paper, we propose a novel method for feature distribution alignment. Specifically, we employ knowledge distillation (KD) to minimize the Kullback–Leibler (KL) divergence between task features, thus promoting greater similarity in the extracted feature distributions. This incentivizes the model to prioritize learning shared parameters and general features across tasks during the training process, rather than task-specific features. Furthermore, we also use an adaptive dropout strategy, which adjusts the training process for the next batch based on the feature distribution of the current task. This method reduces the impact of feature distribution differences during the training iterations. Extensive experiments and analyses demonstrate that our method improves the performance of various meta-learning models in few-shot classification, few-shot fine-grained classification, cross-domain few-shot classification, and non-mutually exclusive few-shot classification.

Generic and Specific Features of Commentaries on Al-Zamakhshari’s (ca. 1143 A.D.) Grammatical Parsing or the Al-Mufassal

حُضي كتاب المفُصَّل للزمخشريّ بعناية فائقة من قبل المحدثين والمتأخرين ،فمنهم من شرح متنه، ومنهم من شرح أبياته ،إلَّا أنَّ من أبرز المؤلفات التي تناولته بالشرح هي(شرح الخوارزميّ، وشرح ابن يعيش، وإيضاح ابن الحاجب، وإقليد تاج الدِّين الجنديّ)، إذ تميَّزت هذه الشروح بمجموعة من السِمات المنهجيَّة والتعليليَّة، منها سِمات عامة، اشترك بها جميع الشرَّاح، ومنها خاصة انفرد بها كل شارح عن غيره. وسَتُبين هذه الدراسة ابرز هذه السِمات، والوقوف على أهم النتائج.

Management of a Bilateral Ectopia Lentis in a Patient with Marfan Syndrome: A Case Report

AIMS: to highlight the management procedure of a bilateral subluxated lens in a patient with marfan syndrome. Case Report: We report a case of a 51-year-old woman, with a family history of Marfan syndrome; who presented for a bilateral subluxated lens, iridodonesis and bilateral macular degeneration. Given the family history, the general physical features and the ocular findings, diagnostic of Marfan syndrome was established. The management of the ectopic lentis was very challenging. An intracapsular lens extraction with posterior chamber intraocular lens fixed to the sclera was performed and the patient regained 2/10 visual acuity on the right eye and remained at counting fingers in the left. Conclusion: Marfan syndrome is a rare condition, with various ocular manifestations. The only guarantee for a better visual outcome is a well-designed therapeutic approach.

Outcomes of ankle arthrodesis in adult patients with ankle osteoarthritis in Kashin-Beck disease.

We retrospectively evaluated the characteristics of these patients and the effectiveness of ankle arthrodesis in the treatment of ankle arthritis caused by Kashin-Beck disease (KBD). A retrospective study of KBD patients with ankle osteoarthritis who underwent ankle arthrodesis between December 2012 and January 2022 was performed. A total of 46 patients were included. The general characteristics, clinical manifestations and imaging features of the patients were recorded and summarized. measured using the VAS score, and ankle function was assessed by the AOFAS ankle-hindfoot score. Multiple subchondral cystic changes were found in 42(91.3%) patients. The VAS scores for both resting and weight-bearing conditions were 6.28 ± 1.30 vs. 2.09 ± 1.12 (P < .001) and 6.87 ± 1.01 vs. 2.17 ± 0.98 (P < .001), respectively. The AOFAS scores were 59.17 ± 5.50 and 88.39 ± 1.42, respectively (P < .001). The subchondral multiple cystic transformation of the ankle KBD has a certain suggestive role.Arthrodesis is an effective method to reduce ankle pain and improve ankle function in KBD patients with ankle osteoarthritis.

Риски внедрения цифровых платформ как отраслевой аспект стратегирования

Even under the current foreign sanctions and external restrictions, Russia’s economic policy is aimed at industrial innovations. However, the ongoing digital transformation remains a serious challenge for timber enterprises: in fact, this industry demonstrates one of the lowest rates of digitalization. Digital transformation has both strategic advantages and strategic risks for such enterprises. If major business fails to consider these risks, the outcome can have long-term negative consequences. Effective strategizing of industrial enterprises needs special methods to identify, describe, and analyze strategic risks. The authors identified the strategic risks of digital transformation and summarized tools that could help timber and forestry enterprises to overcome them. The research relied on the theory and methodology of strategizing developed by Professor V.L. Kvint. According to this approach, a strategy does not identify problems: its main purpose is to identify opportunities, and opportunities always come with risks. The analysis systematized the general and specific features of ERP-systems and digital platforms as key elements of digital transformation. The article introduces opportunities and risks of digital transformation for timber industry enterprises, which must be taken into account by strategists. It also describes an automated process management model as a means of overcoming the risks.

The volumetric ADC histogram analysis in differentiating stage IA endometrial carcinoma from endometrial polyp.

This study aimed to explore the value of apparent diffusion coefficient (ADC) histogram based on whole lesion volume in distinguishing stage IA endometrial carcinoma from the endometrial polyp. MRI of 108 patients with endometrial lesions confirmed by pathology were retrospectively analysed, including 65 cases of stage IA endometrial carcinoma and 43 cases of endometrial polyp. The volumetric ADC histogram metrics and general imaging features were evaluated and measured simultaneously. All the features were compared between the 2 groups. The receiver operating characteristic curve was utilized to evaluate the diagnostic performance. The mean, max, min, and percentiles (10th, 25th, 50th, 75th, 95th) ADC values of endometrial carcinoma were significantly lower than that of polyp (all P < .05). The skewness and kurtosis of ADC values in the endometrial carcinoma group were significantly higher than those in the endometrial polyp group, and the variance of ADC values in the endometrial carcinoma group was lower than those in the endometrial polyp group (all P < .05). Endometrial carcinoma demonstrated more obvious myometrial invasion combined with intralesion haemorrhage than polyp (all P < .05). The 25th percentile of ADC values achieved the largest areas under the curve (0.861) among all the ADC histogram metrics and general imaging features, and the sensitivity and specificity were 83.08% and 76.74%, with the cut-off value of 1.01 × 10-3 mm2/s. The volumetric ADC histogram analysis was an effective method in differentiating endometrial carcinoma from an endometrial polyp. The 25th percentile of ADC values has satisfactory performance for detecting malignancy in the endometrium. The ADC histogram metric based on whole lesion is a promising imaging-maker in differentiating endometrial benign and malignant lesions.

Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning

Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarcinoma, and using CT images to predict the recurrence-free survival (RFS) of lung adenocarcinoma patients post-surgery is of paramount importance in tailoring postoperative treatment plans. Addressing the challenging task of accurate RFS prediction using CT images, this paper introduces an innovative approach based on self-supervised pre-training and multi-task learning. We employed a self-supervised learning strategy known as "image transformation to image restoration" to pretrain a 3D-UNet network on publicly available lung CT datasets to extract generic visual features from lung images. Subsequently, we enhanced the network's feature extraction capability through multi-task learning involving segmentation and classification tasks, guiding the network to extract image features relevant to RFS. Additionally, we designed a multi-scale feature aggregation module to comprehensively amalgamate multi-scale image features, and ultimately predicted the RFS risk score for lung adenocarcinoma with the aid of a feed-forward neural network. The predictive performance of the proposed method was assessed by ten-fold cross-validation. The results showed that the consistency index (C-index) of the proposed method for predicting RFS and the area under curve (AUC) for predicting whether recurrence occurs within three years reached 0.691 ± 0.076 and 0.707 ± 0.082, respectively, and the predictive performance was superior to that of existing methods. This study confirms that the proposed method has the potential of RFS prediction in lung adenocarcinoma patients, which is expected to provide a reliable basis for the development of individualized treatment plans.

Computational model of engagement with stigmatised sentiment: COVID and general vaccine discourse on social media

The growth rate of new social media users continues to surpass new Internet users and new unique mobile phone subscribers and this trend remains consistent over the past 5 years (2019–2023). The most frequently visited types of websites or apps worldwide are chat and messaging, closely followed by social networks and this trend has also remained relatively constant. The dominating role of social media, especially as a source for information seeking, is staggering, particularly during the COVID-19 pandemic. However, the research in Keller et al. (J Mec Internet Res 16:e8, 2014) indicates that not many experts consider social media as a tool for sharing their expertise or for integrating social media into their research efforts. This is a troubling fact, especially considering that stigmatised health narrative are fueled in the face of uncertainty and spread very quickly among the lay population. The latter contributes to the spread of misinformation and, consequently, fosters hesitancy about preventive measures such as vaccines. This research presents new evidence on engagement with stigmatised vaccine discourse on Facebook (Meta), Twitter (X), YouTube and Reddit. Engagement with health-related sentiment can be an important indicator of perceptions regarding preventive measures. The current research can draw the attention of public health experts to the connection between stigmatised discourse and engagement in health discussions, as well as the potential impact of other linguistic features on engagement. It can also guide health authorities and medical professionals in developing effective communication strategies for the general public. Moreover, there are only a handful of studies discussing engagement with socially shared health-care discussions. The research focuses on examining engagement with stigmatised sentiment in vaccination discussions during and prior to the COVID-19 pandemic, using a cross-sectional approach. The study is based on primary data from social media domains, supplemented by secondary data analyses of literature related to the topic. To achieve the research goal, the study employs a multi-method design primarily based on quantitative methods of content analyses, such as Linguistic Inquiry and Word Count (LIWC) (Pennebaker et al. in Linguistic inquiry and word count: Liwc 2001, vol 71. Lawrence Erlbaum Associates, Mahway, 2001) to extract general language features and analyse stigmatised sentiment as the result of previous research findings in Straton et al. (Stigma annotation scheme and stigmatized language detection in health-care discussions on social media. In: Proceedings of The 12th Language Resources and Evaluation Conference (LREC, 2020), pp 1178–1190 (European Language Resources Association, 2020), Straton (Appl Intell, pp 1–26, 2022). Unsupervised K-means clustering methods, along with F-score and Z-score measures, are applied to draw insights from the features. The goal is to explore the phenomena of engagement with socially shared health information, investigate whether stigma can affect the engagement, and identify any other language features that may have an impact. Highly engaging general vaccine discussions before the pandemic appear to be more planned and less emotional. They exhibit reasoning and differentiation typical of more complex discussion sections, incorporating references to authority and family concerns simultaneously. The engaging messages convey stigmatised sentiment, likely shared either with the intent to deceive, or are based on a genuinely biased belief. Some elements of stigmatised discussions indicate deception, while others are more frequent in truthful statements. Engaging content in COVID vaccine discussions, similar to general vaccine content, lacks emotional elements and exhibits high linguistic complexity. However, there seems to be no connection between engagement and stigmatised sentiment. This difference is likely due to the removal of public anti-vaccination pages during the pandemic where anti-vaccination groups moved to discussion section of posts that try to disprove vaccine conspiracy theories. Mixing polarised groups will result in different feedback and engagement with the content. Almost half of the features in COVID and general vaccine discussions have a similar impact on engagement. However, further exploration of the findings suggests that there are distinct differences. Understanding the differences is very important to prevent generalisation errors in the conclusions drawn from the data. Engagement with written text on social media should be measured within a narrow scope: limited regional, demographic and temporal scope. Otherwise, interpretation of the findings risks on being inconsistent.

Time-frequency extreme risk spillovers between COVID-19 news-based panic sentiment and stock market volatility in the multi-layer network: Evidence from the RCEP countries

This study extends extant discussions on regional integration by exploring risk spillovers between news-based panic sentiment and stock market volatility among Regional Comprehensive Economic Partnership (RCEP) members during the COVID-19 pandemic. We innovatively estimate quantile-based spillover indices in both time and frequency domains and construct the multi-layer network. Both static and dynamic risk spillover patterns in the "RCEP panic-stock network" are outlined. It is found that risk spillovers are predominantly transmitted from the "panic-layer network" towards the "volatility-layer network". Besides, regression analyses are employed to investigate whether general features of COVID-19 media reporting affect the spillovers of panic sentiment at the country level. The results show that the magnitude of panic spillovers tend to be positively associated with overall media coverage and negatively with overall media sentiment, especially at the middle and lower quantiles.

Flow interactions lead to self-organized flight formations disrupted by self-amplifying waves

Collectively locomoting animals are often viewed as analogous to states of matter in that group-level phenomena emerge from individual-level interactions. Applying this framework to fish schools and bird flocks must account for visco-inertial flows as mediators of the physical interactions. Motivated by linear flight formations, here we show that pairwise flow interactions tend to promote crystalline or lattice-like arrangements, but such order is disrupted by unstably growing positional waves. Using robotic experiments on “mock flocks” of flapping wings in forward flight, we find that followers tend to lock into position behind a leader, but larger groups display flow-induced oscillatory modes – “flonons” – that grow in amplitude down the group and cause collisions. Force measurements and applied perturbations inform a wake interaction model that explains the self-ordering as mediated by spring-like forces and the self-amplification of disturbances as a resonance cascade. We further show that larger groups may be stabilized by introducing variability among individuals, which induces positional disorder while suppressing flonon amplification. These results derive from generic features including locomotor-flow phasing and nonreciprocal interactions with memory, and hence these phenomena may arise more generally in macroscale, flow-mediated collectives.

Analysis of online rubric platforms: advancing toward erubrics

Rubrics play a crucial role in shaping educational assessment, providing clear criteria for both teaching and learning. The advent of online rubric platforms has the potential to significantly enhance the effectiveness of rubrics in educational contexts, offering innovative features for assessment and feedback through the creation of erubrics. This study presents a comprehensive analysis of 19 online rubric platforms structured around five research questions (RQs) examining the general platform features, rubric design features, rubric implementation features, identifying the stronger online rubric platforms available, and investigating if the platforms support the creation and implementation of erubrics. Our analysis of the design features revealed varying levels of customisation and flexibility across platforms, crucial for effective assessment. Regarding implementation features, we found a mix of online and offline capabilities, with a limited number of platforms offering more advanced features (e.g. collaborative options). Through a detailed scoring system, we identify the platforms that lead innovation in design and implementation. Unfortunately, the vast majority of platforms do not support features for the creation of erubrics. We provide a detailed list of implementation recommendations for teachers, researchers, and platform designers (Appendix F).

Neuronal cell cycle reentry events in the aging brain are more prevalent in neurodegeneration and lead to cellular senescence.

Increasing evidence indicates that terminally differentiated neurons in the brain may recommit to a cell cycle-like process during neuronal aging and under disease conditions. Because of the rare existence and random localization of these cells in the brain, their molecular profiles and disease-specific heterogeneities remain unclear. Through a bioinformatics approach that allows integrated analyses of multiple single-nucleus transcriptome datasets from human brain samples, these rare cell populations were identified and selected for further characterization. Our analyses indicated that these cell cycle-related events occur predominantly in excitatory neurons and that cellular senescence is likely their immediate terminal fate. Quantitatively, the number of cell cycle re-engaging and senescent neurons decreased during the normal brain aging process, but in the context of late-onset Alzheimer's disease (AD), these cells accumulate instead. Transcriptomic profiling of these cells suggested that disease-specific differences were predominantly tied to the early stage of the senescence process, revealing that these cells presented more proinflammatory, metabolically deregulated, and pathology-associated signatures in disease-affected brains. Similarly, these general features of cell cycle re-engaging neurons were also observed in a subpopulation of dopaminergic neurons identified in the Parkinson's disease (PD)-Lewy body dementia (LBD) model. An extended analysis conducted in a mouse model of brain aging further validated the ability of this bioinformatics approach to determine the robust relationship between the cell cycle and senescence processes in neurons in this cross-species setting.

"СОВРЕМЕННОЕ СОСТОЯНИЕ ЭНЕРГЕТИЧЕСКОГО ЗАКОНОДАТЕЛЬСТВА РЕСПУБЛИКИ УЗБЕКИСТАН И ПРАВОВЫЕ ВОПРОСЫ ЕГО СОВЕРШЕНСТВОВАНИЯ "

"This article analyzes the current state of energy legislation of the Republic of Uzbekistan and legal issues of its improvement. The main directions of state energy policy at the present stage and issues of further improvement of energy legislation are substantiated by scientific and legal conclusions. The author pays special attention to the definition of such concepts as “energy,” “alternative energy sources,” “solar energy,” “wind energy,” and “biofuels.” It is established that the characteristic feature of these relations is the transitional periodicity of legal possession of energy as an object of the legal sphere. Therefore, legal relations are regulated by a complex of legal norms related to ecology and energy. The conclusions of the article are based not only on the analysis of the legal framework of the Republic of Uzbekistan but also on the doctrinal positions of foreign experience. Taking into account the peculiarities of the regulation of legal relations related to energy in the Republic of Uzbekistan, the author emphasizes that the existing definitions in the practice of law application, based on the considered examples and terms, are not unique. Therefore, he draws attention to the need to unify the definition of the term “energy resources” in the legal norm for a unified understanding of this phenomenon. The article analyzes the general features of the energy legislation of the Republic of Uzbekistan and develops conclusions and recommendations that can be used in law enforcement and scientific activities to improve energy legislation. "

Integrated encoder-decoder-based wide and deep convolution neural networks strategy for electricity theft arbitration

Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identified through anomalous consumption patterns observed in smart networks. However, a smart model is required for capturing and analysing the data intelligently to accurately detect electricity theft. In the paper, electricity theft has been detected using an encoder-decoder-based classifier that integrates two models of convolutional neural networks (CNN). The aim is to scan the strength of the data and built a smart model that analysed the connections in complex data and determine the pattern of theft. The model comprises three compartments: the auto-encoder, the wide convolutional neural network (1-D CNN model), and the deep convolutional neural network (2-D CNN model). The auto-encoder has been trained on the complex and in-depth linkage between the theft data and the normal data as it removes noise and unnecessary information. The 1-D CNN model gathers relevant connections and general features, while the 2-D CNN model determines the rate at which energy theft occurs and differentiates between the energy-stealing consumers and normal consumers. The efficacy of the approach is underscored by its superiority over traditional deep learning and machine learning techniques. This paper elucidates the distinct advantages and applications of the proposed model in combating electricity theft within smart grid environments.

A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem

This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job shop and assembly shop. It brings about the problem of scheduling association of the related tasks. To this end, a reinforcement learning algorithm with a deep temporal difference network is proposed to minimize the makespan. Firstly, the FDSSP is defined as the mathematical model of the flexible job-shop scheduling problem joined to the assembly constraint level. It is translated into a Markov decision process that directly selects behavioral strategies according to historical machining state data. Secondly, the proposed ten generic state features are input into the deep neural network model to fit the state value function. Similarly, eight simple constructive heuristics are used as candidate actions for scheduling decisions. From the greedy mechanism, optimally combined actions of all machines are obtained for each decision step. Finally, a deep temporal difference reinforcement learning framework is established, and a large number of comparative experiments are designed to analyze the basic performance of this algorithm. The results showed that the proposed algorithm was better than most other methods, which contributed to solving the practical production problem of the manufacturing industry.