Revolutionizing energy practices: Unleashing the power of artificial intelligence in corporate energy transition.

Corporate energy transition is crucial for long-term sustainable development. The widely discussed Artificial Intelligence (AI), as a disruptive technological innovation, is highly potential for enhancing environment performance. However, the specific impact of AI on the process of corporate energy transition and its underlying mechanisms have not been fully explored. This study focuses on A-share listed corporates in Shanghai and Shenzhen stock markets in China spanning from 2011 to 2021. Based on corporate annual report information and information from over 200,000 patent application texts, we innovatively construct indicators for corporate energy transition and AI technology application. Furthermore, we empirically investigate the impact of AI technology on corporate energy transition and its potential mechanisms through combining information asymmetry theory and institutional theory. The empirical results indicate that: 1) AI can drive corporate energy transition and the promoting effect of AI collaborative innovation on corporate energy transition should not be ignored. 2) AI can help corporates achieve energy transition through pathways such as mitigating information asymmetry, reducing financing constraints, adjusting sustainable development concepts and practices. 3) The driving effect of AI on corporate energy transition varies depending on the characteristics of different types of corporates, industries, and regions. This study provides strategic guidance and decision support for business managers and policymakers, assisting both corporates and governments in better utilizing AI technology during the social energy transition process to achieve a dual optimization of environmental and economic goals.

Federated Learning Backdoor Attack Based on Frequency Domain Injection.

Federated learning (FL) is a distributed machine learning framework that enables scattered participants to collaboratively train machine learning models without revealing information to other participants. Due to its distributed nature, FL is susceptible to being manipulated by malicious clients. These malicious clients can launch backdoor attacks by contaminating local data or tampering with local model gradients, thereby damaging the global model. However, existing backdoor attacks in distributed scenarios have several vulnerabilities. For example, (1) the triggers in distributed backdoor attacks are mostly visible and easily perceivable by humans; (2) these triggers are mostly applied in the spatial domain, inevitably corrupting the semantic information of the contaminated pixels. To address these issues, this paper introduces a frequency-domain injection-based backdoor attack in FL. Specifically, by performing a Fourier transform, the trigger and the clean image are linearly mixed in the frequency domain, injecting the low-frequency information of the trigger into the clean image while preserving its semantic information. Experiments on multiple image classification datasets demonstrate that the attack method proposed in this paper is stealthier and more effective in FL scenarios compared to existing attack methods.

Low distortion reversible database watermarking based on hybrid intelligent algorithm.

In many fields, such as medicine and the computer industry, databases are vital in the process of information sharing. However, databases face the risk of being stolen or misused, leading to security threats such as copyright disputes and privacy breaches. Reversible watermarking techniques ensure the ownership of shared relational databases, protect the rights of data owners and enable the recovery of original data. However, most of the methods modify the original data to a large extent and cannot achieve a good balance between protection against malicious attacks and data recovery. In this paper, we proposed a robust and reversible database watermarking technique using a hash function to group digital relational databases, setting the data distortion and watermarking capacity of the band weight function, adjusting the weight of the function to determine the watermarking capacity and the level of data distortion, using firefly algorithms (FA) and simulated annealing algorithms (SA) to improve the efficiency of the search for the location of the watermark embedded and, finally, using the differential expansion of the way to embed the watermark. The experimental results prove that the method maintains the data quality and has good robustness against malicious attacks.

Stable and Fast Deep Mutual Information Maximization Based on Wasserstein Distance.

Deep learning is one of the most exciting and promising techniques in the field of artificial intelligence (AI), which drives AI applications to be more intelligent and comprehensive. However, existing deep learning techniques usually require a large amount of expensive labeled data, which limit the application and development of deep learning techniques, and thus it is imperative to study unsupervised machine learning. The learning of deep representations by mutual information estimation and maximization (Deep InfoMax or DIM) method has achieved unprecedented results in the field of unsupervised learning. However, in the DIM method, to restrict the encoder to learn more normalized feature representations, an adversarial network learning method is used to make the encoder output consistent with a priori positively distributed data. As we know, the model training of the adversarial network learning method is difficult to converge, because there is a logarithmic function in the loss function of the cross-entropy measure, and the gradient of the model parameters is susceptible to the "gradient explosion" or "gradient disappearance" phenomena, which makes the training of the DIM method extremely unstable. In this regard, we propose a Wasserstein distance-based DIM method to solve the stability problem of model training, and our method is called the WDIM. Subsequently, the training stability of the WDIM method and the classification ability of unsupervised learning are verified on the CIFAR10, CIFAR100, and STL10 datasets. The experiments show that our proposed WDIM method is more stable to parameter updates, has faster model convergence, and at the same time, has almost the same accuracy as the DIM method on the classification task of unsupervised learning. Finally, we also propose a reflection of future research for the WDIM method, aiming to provide a research idea and direction for solving the image classification task with unsupervised learning.

Secure Ring Signature Scheme for Privacy-Preserving Blockchain.

Blockchain integrates peer-to-peer networks, distributed consensus, smart contracts, cryptography, etc. It has the unique advantages of weak centralization, anti-tampering, traceability, openness, transparency, etc., and is widely used in various fields, e.g., finance and healthcare. However, due to its open and transparent nature, attackers can analyze the ledger information through clustering techniques to correlate the identities between anonymous and real users in the blockchain system, posing a serious risk of privacy leakage. The ring signature is one of the digital signatures that achieves the unconditional anonymity of the signer. Therefore, by leveraging Distributed Key Generation (DKG) and Elliptic Curve Cryptography (ECC), a blockchain-enabled secure ring signature scheme is proposed. Under the same security parameters, the signature constructed on ECC has higher security in comparison to the schemes using bilinear pairing. In addition, the system master key is generated by using the distributed key agreement, which avoids the traditional method of relying on a trusted third authorizer (TA) to distribute the key and prevents the key leakage when the TA is not authentic or suffers from malicious attacks. Moreover, the performance analysis showed the feasibility of the proposed scheme while the security was ensured.

Association between ambient temperatures and injuries: a time series analysis using emergency ambulance dispatches in Chongqing, China.

BACKGROUND: Global warming and increasing extreme weather have become a severe problem in recent years, posing a significant threat to human health worldwide. Research exploring the link between injury as one of the leading causes of death globally and ambient temperature was lacking. Based on the hourly injury emergency ambulance dispatch (IEAD) records from 2019-2021 in the main urban area of Chongqing, this study explored the role of temperature extremes on the pathogenesis of injury by different mechanisms and identified sensitive populations for different mechanisms of injury. METHODS: In this study, we collected hourly injury emergency ambulance dispatch (IEAD) records from Chongqing Emergency Dispatch Center in the main urban area of Chongqing from 2019 to 2021, and used a distributed lagged nonlinear model (DLNM) with quasi-Poisson distribution to evaluate the association between ambient temperature and IEADs. And the stratified analysis was performed by gender, age and different injury mechanisms to identify susceptible groups. Finally, the attributable burden of ambient extreme temperatures was also investigated. RESULTS: The risk for total IEADs increased significantly at high temperature (32 °C) compared with optimal temperature (9 °C) (CRR: 1.210; 95%CI[1.127,1.300]). The risks of traffic accident injury (CRR: 1.346; 95%CI[1.167,1.552]), beating injury (CRR: 1.508; 95%CI[1.165,1.952]), fall-height injury (CRR: 1.871; 95%CI[1.196-2.926]) and injury of sharp penetration (CRR: 2.112; 95%CI[1.388-3.213]) were significantly increased. At low temperature (7 °C), the risk of fall injury (CRR: 1.220; 95% CI [1.063,1.400]) increased significantly. Lag for 24 hours at extreme low temperature (5 °C), the risk of 18-45 years (RR: 1.016; 95%CI[1.009,1.024]) and over 60 years of age (RR: 1.019; 95%CI[1.011,1.025]) increased significantly. The effect of 0 h delay in extreme high temperature (36 °C) on males aged 18-45 years (RR: 1.115; 95%CI[1.071,1.162]) and 46-59 years (RR: 1.069; 95%CI[1.023,1.115]) had significant impact on injury risk. CONCLUSIONS: This study showed that ambient temperature was significantly related to the risk of injury, and different mechanisms of injury were affected differently by extreme temperature. The increasing risk of traffic accident injury, beating injury, fall-height injury and sharp penetrating injury was associated with extreme heat, while fall injury was associated with extreme cold. The risk of injury in high temperature environment was mainly concentrated in males and young adults. The results of this study can help to identify the sensitive population with different injury mechanisms in extreme temperature environment, and provide reference for public health emergency departments to respond to relevant strategies in extreme temperature environment to minimize the potential risk to the public.

Wasserstein Distance-Based Deep Leakage from Gradients.

Federated learning protects the privacy information in the data set by sharing the average gradient. However, "Deep Leakage from Gradient" (DLG) algorithm as a gradient-based feature reconstruction attack can recover privacy training data using gradients shared in federated learning, resulting in private information leakage. However, the algorithm has the disadvantages of slow model convergence and poor inverse generated images accuracy. To address these issues, a Wasserstein distance-based DLG method is proposed, named WDLG. The WDLG method uses Wasserstein distance as the training loss function achieved to improve the inverse image quality and the model convergence. The hard-to-calculate Wasserstein distance is converted to be calculated iteratively using the Lipschit condition and Kantorovich-Rubinstein duality. Theoretical analysis proves the differentiability and continuity of Wasserstein distance. Finally, experiment results show that the WDLG algorithm is superior to DLG in training speed and inversion image quality. At the same time, we prove through the experiments that differential privacy can be used for disturbance protection, which provides some ideas for the development of a deep learning framework to protect privacy.

Image Adversarial Example Generation Method Based on Adaptive Parameter Adjustable Differential Evolution.

Adversarial example generation techniques for neural network models have exploded in recent years. In the adversarial attack scheme for image recognition models, it is challenging to achieve a high attack success rate with very few pixel modifications. To address this issue, this paper proposes an adversarial example generation method based on adaptive parameter adjustable differential evolution. The method realizes the dynamic adjustment of the algorithm performance by adjusting the control parameters and operation strategies of the adaptive differential evolution algorithm, while searching for the optimal perturbation. Finally, the method generates adversarial examples with a high success rate, modifying just a very few pixels. The attack effectiveness of the method is confirmed in CIFAR10 and MNIST datasets. The experimental results show that our method has a greater attack success rate than the One Pixel Attack based on the conventional differential evolution. In addition, it requires significantly less perturbation to be successful compared to global or local perturbation attacks, and is more resistant to perception and detection.

A Parallel Multi-Modal Factorized Bilinear Pooling Fusion Method Based on the Semi-Tensor Product for Emotion Recognition.

Multi-modal fusion can exploit complementary information from various modalities and improve the accuracy of prediction or classification tasks. In this paper, we propose a parallel, multi-modal, factorized, bilinear pooling method based on a semi-tensor product (STP) for information fusion in emotion recognition. Initially, we apply the STP to factorize a high-dimensional weight matrix into two low-rank factor matrices without dimension matching constraints. Next, we project the multi-modal features to the low-dimensional matrices and perform multiplication based on the STP to capture the rich interactions between the features. Finally, we utilize an STP-pooling method to reduce the dimensionality to get the final features. This method can achieve the information fusion between modalities of different scales and dimensions and avoids data redundancy due to dimension matching. Experimental verification of the proposed method on the emotion-recognition task using the IEMOCAP and CMU-MOSI datasets showed a significant reduction in storage space and recognition time. The results also validate that the proposed method improves the performance and reduces both the training time and the number of parameters.

The Impact of Financial Deepening on Carbon Reductions in China: Evidence from City- and Enterprise-Level Data.

This study extends the limited evidence of the China context by establishing a panel fixed-effect model to identify the nexus between financial deepening and carbon emissions. Using newly compiled city-level (287 prefecture-level and above cities) and enterprise-level (resource enterprises listed on the Chinese A-shares) datasets from 2007 to 2019, this study quantitatively evaluated finance deepening and analysed the impact of financial deepening on carbon emissions in China, with a particular consideration of green innovation. Our results document that financial deepening contributes to carbon reductions, as shown by the considerably decreased carbon dioxide (CO2) emissions. Both the city-level and enterprise-level estimates argue that financial deepening has a promoting effect on green innovation. Stimulating green innovation is identified as an important mechanism through which financial deepening can contribute to carbon reductions. Policy implications are presented based on the empirical results.

Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors.

Sensor placement is an important factor that may significantly affect the localization performance of a sensor network. This paper investigates the sensor placement optimization problem in three-dimensional (3D) space for angle of arrival (AOA) target localization with Gaussian priors. We first show that under the A-optimality criterion, the optimization problem can be transferred to be a diagonalizing process on the AOA-based Fisher information matrix (FIM). Secondly, we prove that the FIM follows the invariance property of the 3D rotation, and the Gaussian covariance matrix of the FIM can be diagonalized via 3D rotation. Based on this finding, an optimal sensor placement method using 3D rotation was created for when prior information exists as to the target location. Finally, several simulations were carried out to demonstrate the effectiveness of the proposed method. Compared with the existing methods, the mean squared error (MSE) of the maximum a posteriori (MAP) estimation using the proposed method is lower by at least 25% when the number of sensors is between 3 and 6, while the estimation bias remains very close to zero (smaller than 0.15 m).

A Multi-Modal Fusion Method Based on Higher-Order Orthogonal Iteration Decomposition.

Multi-modal fusion can achieve better predictions through the amalgamation of information from different modalities. To improve the performance of accuracy, a method based on Higher-order Orthogonal Iteration Decomposition and Projection (HOIDP) is proposed, in the fusion process, higher-order orthogonal iteration decomposition algorithm and factor matrix projection are used to remove redundant information duplicated inter-modal and produce fewer parameters with minimal information loss. The performance of the proposed method is verified by three different multi-modal datasets. The numerical results validate the accuracy of the performance of the proposed method having 0.4% to 4% improvement in sentiment analysis, 0.3% to 8% improvement in personality trait recognition, and 0.2% to 25% improvement in emotion recognition at three different multi-modal datasets compared with other 5 methods.

QuantifyPolarity, a new tool-kit for measuring planar polarized protein distributions and cell properties in developing tissues.

The coordination of cells or structures within the plane of a tissue is known as planar polarization. It is often governed by the asymmetric distribution of planar polarity proteins within cells. A number of quantitative methods have been developed to provide a readout of planar polarized protein distributions. However, previous planar polarity quantification methods can be affected by variation in cell geometry. Hence, we developed a novel planar polarity quantification method based on Principal Component Analysis (PCA) that is shape insensitive. Here, we compare this method with other state-of-the-art methods on simulated models and biological datasets. We found that the PCA method performs robustly in quantifying planar polarity independently of variation in cell geometry and other image conditions. We designed a user-friendly graphical user interface called QuantifyPolarity, equipped with three polarity methods for automated quantification of polarity. QuantifyPolarity also provides tools to quantify cell morphology and packing geometry, allowing the relationship of these characteristics to planar polarization to be investigated. This tool enables experimentalists with no prior computational expertise to perform high-throughput cell polarity and shape analysis automatically and efficiently.

An Efficient Plaintext-Related Chaotic Image Encryption Scheme Based on Compressive Sensing.

With the development of mobile communication network, especially 5G today and 6G in the future, the security and privacy of digital images are important in network applications. Meanwhile, high resolution images will take up a lot of bandwidth and storage space in the cloud applications. Facing the demands, an efficient and secure plaintext-related chaotic image encryption scheme is proposed based on compressive sensing for achieving the compression and encryption simultaneously. In the proposed scheme, the internal keys for controlling the whole process of compression and encryption is first generated by plain image and initial key. Subsequently, discrete wavelets transform is used in order to convert the plain image to the coefficient matrix. After that, the permutation processing, which is controlled by the two-dimensional Sine improved Logistic iterative chaotic map (2D-SLIM), was done on the coefficient matrix in order to make the matrix energy dispersive. Furthermore, a plaintext related compressive sensing has been done utilizing a measurement matrix generated by 2D-SLIM. In order to make the cipher image lower correlation and distribute uniform, measurement results quantified the 0∼255 and the permutation and diffusion operation is done under the controlling by two-dimensional Logistic-Sine-coupling map (2D-LSCM). Finally, some common compression and security performance analysis methods are used to test our scheme. The test and comparison results shown in our proposed scheme have both excellent security and compression performance when compared with other recent works, thus ensuring the digital image application in the network.

Miniaturized all fiber probe for optical coherence tomography and pH detection of biological tissue.

We present a novel all-fiber probe with 710-µm outside diameter for combined optical coherence tomography and pH detection. In cancer surgery, a significant challenge is how to completely remove the malignant tumor without cutting too much normal tissue. The difference between cancer tissue and normal tissue not only lies in morphology and structure but also in tissue pH, where malignant tissue has a lower pH. This dual-modality probe combined optical coherence tomography and pH detection of biological tissue, is expected to determine whether the tissue is cancerous quickly and accurately. The probe utilizes a typical three-segment structure (double-clad fiber - no-core fiber - graded-index fiber). We obtained a lateral resolution of ~10.6 µm, a working distance of ~506 µm and a pH measurement accuracy of 0.01 pH unit for the probe. The performance of the all-fiber probe was verified through an ex vivo experiment using the porcine brain specimen.

Underdetermined DOA Estimation for Wideband Signals via Focused Atomic Norm Minimization.

In underwater acoustic signal processing, direction of arrival (DOA) estimation can provide important information for target tracking and localization. To address underdetermined wideband signal processing in underwater passive detection system, this paper proposes a novel underdetermined wideband DOA estimation method equipped with the nested array (NA) using focused atomic norm minimization (ANM), where the signal source number detection is accomplished by information theory criteria. In the proposed DOA estimation method, especially, after vectoring the covariance matrix of each frequency bin, each corresponding obtained vector is focused into the predefined frequency bin by focused matrix. Then, the collected averaged vector is considered as virtual array model, whose steering vector exhibits the Vandermonde structure in terms of the obtained virtual array geometries. Further, the new covariance matrix is recovered based on ANM by semi-definite programming (SDP), which utilizes the information of the Toeplitz structure. Finally, the Root-MUSIC algorithm is applied to estimate the DOAs. Simulation results show that the proposed method outperforms other underdetermined DOA estimation methods based on information theory in term of higher estimation accuracy.

Assembly pattern of multicomponent supramolecular oleogel composed of ceramide and lecithin in sunflower oil: self-assembly or self-sorting?

Ceramide (CER) is a novel food-grade organogelator with beneficial health effects. Lecithin (LEC) is not an effective gelator; however, it may alter the crystal morphology of the host gelator in a multicomponent system. In this paper, LEC and CER were mixed at various molar ratios in sunflower oil leading to different gelation behaviors. It was interesting since in this multicomponent system, gels formed when there was more less-effective gelator (LEC), while gels hardly formed when there was more effective gelator (CER). This drew our attention since there might not be only one kind of assembly mode between the LEC and the CER. A comprehensive rheological investigation was conducted. It was found that at specific ratios (6L4C and 5L5C), strong gels (G' > 1.0 × 105 Pa) formed with superior oil binding capacity (up to 99.84%). Meanwhile, these gels exhibited higher tolerance level to permanent deformation than the monocomponent gel. However, weak gels were observed off the optimal ratio (8L2C, 7L3C, 4L6C and 3L7C). The crystal morphology of gels drastically changed with change in gelator proportion. Short needle-like crystals and small rosette crystals were observed in 6L4C and 5L5C, respectively, while other samples exhibited spherulite-shaped crystals (8L2C, 7L3C, 4L6C, and 3L7C), which differed from any of the monocomponent gel structures (10L0C and 0L10C). Results from differential scanning calorimetry and polarized light microscopy suggested that the macroscopic properties are determined by the morphology and distribution of crystals rather than the crystallinity of the matrix. Fourier transform infrared spectroscopy results indicated the presence of van der Waals forces and the formation of hydrogen bonding between the phosphate of the LEC and the amide group of the CER. The above results indicated that the LEC and CER co-assembled at approximately equal molar ratio, and the redundant LEC or CER at other ratios self-sorted to combine with the co-assembled fibers by lateral association, leading to potentially different underlying microstructures. These multicomponent supramolecular oleogels with high tunability may further broaden their applicability in various healthy lipid-based product formats.

Modification of atrioventricular node in a special condition treating paroxysmal supraventricular tachycardia.

Modification of atrioventricular node is a usual and necessary operation to cure atrioventricular nodal reentrant tachycardia (AVNRT). In this operation, atrioventricular block is the most severe complication and its prevention is of our great concern. This complication always occurs under some special circumstances with potential risk. So, it is very important to realize such conditions, as in this paper. A patient with paroxysmal palpitation for 10 years, aggravating to shortness of breath with chest distress for 1 year; cardiac electrophysiological examination found slow conduction in both antegrade and retrograde paths of reentrant loop, and typical AVNRT could be induced. During effective ablation there was no junctional rhythm. In some special cases, modification of atrioventricular node should not only rely on the junctional rhythm to determine the ablation effect, but also on the time of cardiac electrophysiological examination, as such to avoid the severe complication of atrioventricular block caused by excessive ablation.