Wide Network Research Articles

The future is local? Contextualizing municipal agendas on climate change in Chile

Social science literature on the political strategies used to mitigate and adapt to climate change has discussed the possibilities of local administrations and the multifaceted obstacles in their path. This case study looks at Chile, where debates about local climate agendas and policies have gained political relevance in recent years. It considers municipal administrations, with a focus on their agendas and the (potential) challenges they face. Building on the evaluation of literature, policy documents, and semi-structured interviews, the study concludes that local administrations have gained leeway for action due to changes in national regulation and integration into wider networks, but tight budgets for dedicated climate policies persist. The governance structures in which local climate agendas are embedded, as well as contextual constraints, reflect Chile’s institutional and neoliberal politeconomic arrangements.

A lightweight multi-feature fusion vision transformer bearing fault diagnosis method with strong local sensing ability in complex environments

Fault diagnosis of rolling bearings in complex environments is a difficult problem. First, the median filter can remove the noise in the vibration signals, however, it cannot adaptively adjust the filter weights according to the input signals. Second, the popular vision transformer (ViT) cannot extract local feature information under complex conditions and has a large number of parameters, which result in increased computational complexity. To solve these problems, a lightweight multi-feature fusion ViT bearing fault diagnosis method with strong local awareness in complex environments is proposed. Firstly, to learn the features and statistical distributions of the input signals, the gradient descent method is used to continuously and iteratively update the weights and filter the signals. Then, to better extract critical local fault information, a local sensing module is constructed using multi-scale wide convolutional neural network. Finally, an improved lightweight multi-feature fusion ViT is constructed to perform global feature extraction and fault identification. The results show that the proposed method has better noise reduction effect and feature extraction ability, and can accurately identify the fault types under the complex environments.

XCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model

BackgroundPredicting protein–protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, which contain diverse information, including structural, evolutionary, and functional aspects, has not been fully exploited. Additionally, there is a significant need for a comprehensive neural network capable of efficiently extracting these multifaceted representations.ResultsAddressing this gap, we propose xCAPT5, a novel hybrid classifier that uniquely leverages the T5-XL-UniRef50 protein large language model for generating rich amino acid embeddings from protein sequences. The core of xCAPT5 is a multi-kernel deep convolutional siamese neural network, which effectively captures intricate interaction features at both micro and macro levels, integrated with the XGBoost algorithm, enhancing PPIs classification performance. By concatenating max and average pooling features in a depth-wise manner, xCAPT5 effectively learns crucial features with low computational cost.ConclusionThis study represents one of the initial efforts to extract informative amino acid embeddings from a large protein language model using a deep and wide convolutional network. Experimental results show that xCAPT5 outperforms recent state-of-the-art methods in binary PPI prediction, excelling in cross-validation on several benchmark datasets and demonstrating robust generalization across intra-species, cross-species, inter-species, and stringent similarity contexts.

Legitimate governance in international politics: Towards a relational theory of legitimation

AbstractHow do governing actors in international politics become legitimised? Current approaches to the study of legitimation do not fully account for the complexities of governance in contemporary international and global politics because they pre-specify ‘sources’ of legitimacy and treat change in audience expectations towards rightful rule as exogenous to legitimation processes. Instead, this article synthesises existing models of legitimation with relational theory to argue that constellations of institutional complexities necessitate an analytical focus on audiences and their expectations as embedded in governance networks. It then provides a relational theory of legitimation, emphasising the mechanisms undergirding legitimation: legitimation should be conceptualised as a process of congruence-finding between actors’ normative expectations. A governance relation might be influenced towards greater or lesser congruence via several mechanisms working at the level of the relation and the wider network, with more congruence giving rise to stabler governance practices. In this way, the theory builds upon legitimation scholarship by developing pathways to investigate legitimation across the varied contexts of international politics: it avoids a normative background theory of legitimacy sources and provides an improved framework for understanding change in the legitimacy of institutions over time by considering endogenous mechanisms of legitimation.

Formative Spaces of Empire: Masculinities and Outdoor Experiences ca. 1860–1960

ABSTRACT This special issue integrates gender analysis into the global history of outdoor activities in the Age of Empire by focusing on masculinities, a field that has received renewed attention from scholars. The premise of the special issue is that social constructions of masculinities in imperial settings functioned twofold. They operated simultaneously as methods to spread Western-colonial hegemonic values and as a means to expand territorial domains into far-off lands. In examining outdoor experiences, without taking the dominance of ‘imperial’ men over non-Europeans for granted, the contributions here presented develop an intersectional understanding of the conditions in which fashioning and self-perception of masculine roles were constantly contested and negotiated. Outdoor experiences, as seen in this special issue, were formative spaces of empire: while made possible thanks to the wide imperial networks in the colonial world, they existed on the margins of imperial rule. At the ‘frontier’ and in colonial battle-grounds, but also as leisure or free-time activities in transcultural contexts, outdoor experiences served to transform boys into men, and for men to test and perform hegemonic ideas of manhood and hence of imperial power. The essays are in two sections that highlight the dual processes of being and becoming ‘manly’ in the imperial outdoors. The first four contributions focus on archetypical roles of adult men in empires: the mountaineer, the hunter, the sportsman and the soldier. The second section approaches cases of scouting as formative spaces for boys in contexts of decolonisation in the early 1900s. The case-studies included in this special issue cover multiple imperial formations from the American Midwest, the Middle East to the British and Dutch Indies. These diverse cases serve to open up often Anglo-centric historiographies of gender and empire by emphasising the global momentum of new masculinities that were embedded in a trans-imperial fashion between ca. 1860 and 1960.

Primary Care Physicians In Medicare Advantage Were Less Costly, Provided Similar Quality Versus Regional Average.

The use of many services is lower in Medicare Advantage (MA) compared with traditional Medicare, generating cost savings for insurers, whereas the quality of ambulatory services is higher. This study examined the role of selective contracting with providers in achieving these outcomes, focusing on primary care physicians. Assessing primary care physician costliness based on the gap between observed and predicted costs for their traditional Medicare patients, we found that the average primary care physician in MA networks was $433 less costly per patient (2.9percent of baseline) compared with the regional mean, with less costly primary care physicians included in more networks than more costly ones. Favorable selection of patients by MA primary care physicians contributed partially to this result. The quality measures of MA primary care physicians were similar to the regional mean. In contrast, primary care physicians excluded from all MA networks were $1,617 (13.8percent) costlier than the regional mean, with lower quality. Primary care physicians in narrow networks were $212 (1.4percent) less costly than those in wide networks, but their quality was slightly lower. These findings highlight the potential role of selective contracting in reducing costs in the MA program.

The firm-level costs of utilizing free trade agreements

The number of free trade agreements (FTAs) has surged in recent years. In order to benefit from lower import duties, firms must apply for preferential customs treatment and comply with rules of origin requirements. This is costly and explains why preference utilization rates are typically far below 100 percent. Analyzing Switzerland’s wide FTA network based on a novel data set on all import transactions, our findings shed light on how sourcing requirements affect FTA utilization. Using a revealed preference criterion, we identify a fixed cost component of the utilization which firms avoid especially for urgent shipments. We also find limited evidence of learning effects and provide well-identified estimates of the restrictive effects of rules of origin.

PENENTUAN STRATEGI PEMASARAN HASIL TEMBAKAU DI TENGAH NAIKNYA TARIF CUKAI DAN PEREDARAN ROKOK ILEGAL

The tobacco products industry in Indonesia is facing two big challenges: increasing excise rates and the circulation of illegal cigarettes. An increase in excise rates increases cigarette prices and can reduce demand. On the other hand, illegal cigarettes offer cheaper prices and are competitors to legal products. CV Gagak Hitam is a cigarette company that was founded in 2004 and is still running the tobacco products industry in the form of hand-rolled kretek cigarettes (SKT) and machine-made kretek cigarettes (SKM) amidst rising excise rates and the circulation of illegal cigarettes. This research focuses on determining marketing strategies for tobacco products at CV. Black Crow uses SWOT analysis. The analysis results show that CV. Black Crow has several strengths, such as a strong brand, wide distribution network, and consumer loyalty. However, CV Gagak Hitam also has several weaknesses, such as high prices and a negative image of tobacco products related to health. Opportunities that can be utilized by CV. Black Crow is increasing public awareness of health and the development of digital technology. The threats facing this industry are increasing excise rates, the circulation of illegal cigarettes, and increasingly stringent government regulations. Marketing strategies that can be implemented by CV. Gagak Hitam in the midst of this situation is developing innovative products at competitive prices, strengthening brand image and increasing consumer awareness, utilizing digital technology to reach consumers and increase marketing efficiency, as well as working with the government to overcome the circulation of illegal cigarettes. Keywords: tobacco products; excise rates; illegal cigarettes; marketing strategy swot analysis.

Зарубежный опыт и перспективы использования электронного формата уголовных дел в Российской Федерации

The article discusses the foreign experience of digitalization of the preliminary investigation. In conclusion, the authors come to the conclusion that, given the predominance of the conservative view of law enforcement officers and the legislator of the Russian Federation, only minor steps are currently taking place towards the digitalization of the preliminary investigation in our country. At the same time, it was found that the introduction of various information systems and workstations into practical activities, which involved the creation of a single system, a wide coverage network, did not occur due to the lack of a single request from the system of investigative bodies and technical capabilities, as well as the absence of an urgent need to change the order of work by investigators and interrogators.

Gaya Hidup Remaja Terhadap Belanja Online Shop Menggunakan Aplikasi Shopee Kota Pontianak

The influence of the internet on the community environment is very influential, because the internet can bring a very wide network for customers or teenagers to choose alternative shopping online. The support of an online application system can make it easier for teenagers to see applications just shopping online in the onlineshop application, so that the attraction to shop through online or e-commerce can be an attraction for teenagers to facilitate them in the process of selecting and purchasing the desired product. Basically, the shopee application is an online shopping platform that is in great demand because it is a platform that is tailored for the region and provides an easy and fast online shopping experience for teenagers through strong support and logistics. The purpose of this study is to find out how the lifestyle of teenagers in Pontianak City towards online shopping using the shopee application. The research method that will be used in this study is using quantitative methods, with this research will use survey methods. Research hypothesis: the research hypothesis used is descriptive analysis carried out through testing descriptive hypotheses. The result of the analysis is whether the research hypothesis can be generalized or not.

Diesel engine fault diagnosis for multiple industrial scenarios based on transfer learning

Fault diagnosis based on data-driven intelligence has recently attracted extensive interest owing to the rapid development of big data and deep-learning algorithms. However, when the amount of faulty data is limited, deep learning training is prone to overfitting. When the application scenario is changed, the generalization ability of the trained network is affected. In this study, a fault diagnosis architecture based on deep transfer learning is proposed to work with limited data and transfer between multiple scenarios. A wide convolution kernel convolutional long short-term memory neural network (WCL) was used to improve the feature extraction ability of fault data from a diesel engine with a low signal-to-noise ratio. A multiple transfer learning scheme based on WCL was further adopted to transfer the well-trained diagnostic knowledge of large-scale labeled source domain data to the target domain with limited samples. In addition, for diesel engines for various purposes, the knowledge transferability between different scenarios was studied. The algorithm evaluates the transfer performance of four different domains when the sample is insufficient, including the cross-fault type, cross-equipment type, cross-fault degree, and cross-working conditions. The results show the proposed method is proven with high noise immunity improves the accuracy of small sample cross-domain diagnosis and provides an optimal transfer scheme suitable for diesel engine fault signals.

Effects of Resistance Training on Leg-strength and Back-strength in 18-25 Years Women

In the contest of human life there is essentiality of being good. Being good not only means to feel good but also to look good and live well. Now a day’s physical education spreads the wide network in the worldwide. It mainly having two faces they are science and arts. The resistance training is one of the scientific training method in physical education and sports sciences. In this study the researcher has carried on experiments to investigate the effects of resistance training on leg strength and back strength variables before, during and after resistance training. The researcher has taken 50 subjects, and they are divided into two group one is experimental and anther one is control group. The experimental training phase of twelve weeks, the subjects were given resistance training programme trice a week. The exercise are Shoulder Press, Bent-Knee Sit-Ups, Half Squat, Over head press and Dead lift. The collected data’s were analyzed by using SPSS software version 2.0. The result of the study shows that there were significant differences obtained on selected Motor fitness components such as Leg Strength and Back Strength variables of Resistance Training on women in the age group of 18 to 25 years.

Bearing fault diagnosis based on variational autoencoder and non-local block wide kernel convolutional neural network

At present, convolutional neural network (CNN) is widely applied to bearing fault diagnosis However, the diagnosis performance will descend under the strong noise condition in the real industrial environment. Therefore, a denoising method named non-local block wide kernel CNN (NLBWCNN) is proposed based on wide convolution kernel and non-local block. Additionally, the data in the mechanical fault state is less than that in the health state in actual industrial production, which leads to the data imbalance problem. However, the fault classifier based on CNN needs a large amount of balanced data to train. Otherwise, it will not be fully trained, and thus its generalization ability will be affected. As a result, a method called VAE-NLBWCNN (variational autoencoder and NLBWCNN) is proposed for diagnosing bearing faults. The method employs variational autoencoder balanced the fault data. And then, the NLBWCNN is utilized to denoise and classify the fault data. The proposed VAE-NLBWCNN method is validated on three bearing datasets. The comparative experiments demonstrate that the proposed method can effectively expand unbalanced data and achieve the best performance in various noise conditions.

Pathophysiology of cluster headache: From the trigeminovascular system to the cerebral networks.

Despite advances in neuroimaging and electrophysiology, cluster headache's pathogenesis remains unclear. This review will examine clinical neurophysiology studies, including electrophysiological and functional neuroimaging, to determine if they might help us construct a neurophysiological model of cluster headache. Clinical, biochemical, and electrophysiological research have implicated the trigeminal-parasympathetic system in cluster headache pain generation, although the order in which these two systems are activated, which may be somewhat independent, is unknown. Electrophysiology and neuroimaging have found one or more central factors that may cause seasonal and circadian attacks. The well-known posterior hypothalamus, with its primary circadian pacemaker suprachiasmatic nucleus, the brainstem monoaminergic systems, the midbrain, with an emphasis on the dopaminergic system, especially when cluster headache is chronic, and the descending pain control systems appear to be involved. Functional connection investigations have verified electrophysiological evidence of functional changes in distant brain regions connecting to wide cerebral networks other than pain. We propose that under the impact of external time, an inherited misalignment between the primary circadian pacemaker suprachiasmatic nucleus and other secondary extra- suprachiasmatic nucleus clocks may promote disturbance of the body's internal physiological clock, lowering the threshold for bout recurrence.

Neural Network Gaussian Processes by Increasing Depth.

Recent years have witnessed an increasing interest in the correspondence between infinitely wide networks and Gaussian processes. Despite the effectiveness and elegance of the current neural network Gaussian process theory, to the best of our knowledge, all the neural network Gaussian processes (NNGPs) are essentially induced by increasing width. However, in the era of deep learning, what concerns us more regarding a neural network is its depth as well as how depth impacts the behaviors of a network. Inspired by a width-depth symmetry consideration, we use a shortcut network to show that increasing the depth of a neural network can also give rise to a Gaussian process, which is a valuable addition to the existing theory and contributes to revealing the true picture of deep learning. Beyond the proposed Gaussian process by depth, we theoretically characterize its uniform tightness property and the smallest eigenvalue of the Gaussian process kernel. These characterizations can not only enhance our understanding of the proposed depth-induced Gaussian process but also pave the way for future applications. Lastly, we examine the performance of the proposed Gaussian process by regression experiments on two benchmark datasets.

Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type Nonlinearities.

The paradigm of stochastic antiresonance is considered for a class of nonlinear systems with sector bounded nonlinearities. Such systems arise in a variety of situations such as in engineering applications, in physics, in biology, and in systems with more general nonlinearities, approximated by a wide neural network of a single hidden layer, such as the error equation of Hopfield networks with respect to equilibria or visuo-motor tasks. It is shown that driving such systems with a certain amount of state-multiplicative noise, one can stabilize noise-free unstable systems. Linear-Matrix-Inequality-based stabilization conditions are derived, utilizing a novel non-quadratic Lyapunov functional and a numerical example where state-multiplicative noise stabilizes a nonlinear system exhibiting chaotic behavior is demonstrated.

The Evolution of Scottish Enlightenment Publishing

AbstractThis article uses large-scale bibliographic data to extract and analyse the works, authors, and publishers of the Scottish Enlightenment. By doing so, we aim to encompass a wider scope and definition of Scottish Enlightenment publishing, contextualizing both the major and the lesser-known publishers. We reveal two competing models for key Scottish publishers: those working in Scotland, publishing works that were printed later in London; and those working in London, printing Scottish works. We show that the careers of key publishers such as Andrew Millar (1705–68) should be considered in relational terms: that Millar must be understood in the context of his wider network, taking into account a longer view of the publishing landscape both before and after his career. Moreover, we establish the relevance of subsequent editions of existing works for the understanding of eighteenth-century publishing. The article also argues for an agnostic view of the ‘Scottish Enlightenment text’, one which considers the features of individual texts rather than a priori assumptions about canonical works. Consequently, we show that the significance of works of scientific improvement evolves and becomes intertwined with education, literature, philosophy, and history over time; resulting in a convergence of practice, theory, and literary expression.

A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance

As a vital constituent of rotating machinery, rolling bearings assume a pivotal function in ensuring the stable operation of equipment. Recently, deep learning (DL)-based methods have been able to diagnose bearing faults accurately. However, in practical applications, the severe data imbalance caused by the limited availability of fault data compared to the abundance of healthy data poses challenges to the effective training of DL models, leading to a decrease in diagnostic accuracy. In this paper, a bearing fault diagnosis method with the improved residual Unet diffusion model (IResUnet-DM) based on a data generation strategy is proposed to solve the extreme data imbalance. Initially, a deep feature extraction network named improved residual Unet is built, which effectively enhances the information learning ability from vibration signals of the Unet network by one-dimensional residual block and self-attention block. Furthermore, the IResUnet-DM is constructed, which generates vibration signals under extreme data imbalance based on a probability model. The variational bound on the negative log-likelihood of the distribution of generated data was optimized to make the generated data similar to the real data distribution. Finally, wide deep convolutional neural network and one-dimensional ResNet classification networks were used for fault identification to verify the validity and generalization of the IResUnet-DM. Experiment results at different data imbalance rates on two bearing datasets demonstrate that the proposed method can effectively improve fault diagnosis accuracy under extreme data imbalances and outperform the comparison method.

Power system monitoring for electrical disturbances in wide network using machine learning

Due to infrastructure developments, wide disturbances have occurred in the power system. There is a need for intelligent monitoring systems across wide power networks for the stability and security of systems. A significant challenge in a comprehensive power monitoring system is identifying the noises in electrical measurements and oscillatory errors. In this research, the disturbances in the power system are monitored using principal component analysis with a Support vector machine and Extreme Learning Machine (ELM) for analyzing the monitored data. In this work, PCA has been used to reduce the curse of dimensionality of the original data. Then, SVM was used to select the relevant and essential features from the disturbance signals. These selected features are fed as input into the Extreme learning machine to classify the power quality events. This machine learning advantage is that it can analyze many wide-area variables in real time and reduce the masking effect of the oscillatory trends and noise on disturbances. Compared to the existing feature selection and classification of PQ disturbance data, the proposed model secured an improved accuracy of 99.16%, and the comparison results prove the model's effectiveness.

Deep convolutional neural networks information fusion and improved whale optimization algorithm based smart oral squamous cell carcinoma classification framework using histopathological images

AbstractThe most prevalent type of cancer worldwide is mouth cancer. Around 2.5% of deaths are reported annually due to oral cancer in 2023. Early diagnosis of oral squamous cell carcinoma (OSCC), a prevalent oral cavity cancer, is essential for treating and recovering patients. A few computerized techniques exist but are focused on traditional machine learning methods, such as handcrafted features. In this work, we proposed a fully automated architecture based on Self‐Attention convolutional neural network and Residual Network information fusion and optimization. In the proposed framework, the augmentation process is performed on the training and testing samples, and then two developed deep models are trained. A self‐attention MobileNet‐V2 model is developed and trained using an augmented dataset. In parallel, a Self‐Attention DarkNet‐19 model is trained on the same dataset, whereas the hyperparameters have been initialized using the whale optimization algorithm (WOA). Features are extracted from the deeper layers of both models and fused using a canonical correlation analysis (CCA) approach. The CCA approach is further optimized using an improved WOA version named Quantum WOA that removes the irrelevant features and selects only important ones. The final selected features are classified using neural networks such as wide neural networks. The experimental process is performed on the augmented dataset that includes two sets: 100× and 400×. Using both sets, the proposed method obtained an accuracy of 98.7% and 96.3%. Comparison is conducted with a few state‐of‐the‐art (SOTA) techniques and shows a significant improvement in accuracy and precision rate.