Reviewerly/exHarmony huggingface dataset:
A Systematic Review of Blockchain Technology Adoption Barriers and Enablers for Smart and Sustainable Agriculture

Smart and sustainable agricultural practices are more complex than other industries as the production depends on many pre- and post-harvesting factors which are difficult to predict and control. Previous studies have shown that technologies such as blockchain along with sustainable practices can achieve smart and sustainable agriculture. These studies state that there is a need for a reliable and trustworthy environment among the intermediaries throughout the agrifood supply chain to achieve sustainability. However, there are limited studies on blockchain technology adoption for smart and sustainable agriculture. Therefore, this systematic review uses the PRISMA technique to explore the barriers and enablers of blockchain adoption for smart and sustainable agriculture. Data was collected using exhaustive selection criteria and filters to evaluate the barriers and enablers of blockchain technology for smart and sustainable agriculture. The results provide on the one hand adoption enablers such as stakeholder collaboration, enhance customer trust, and democratization, and, on the other hand, barriers such as lack of global standards, industry level best practices and policies for blockchain adoption in the agrifood sector. The outcome of this review highlights the adoption barriers over enablers of blockchain technology for smart and sustainable agriculture. Furthermore, several recommendations and implications are presented for addressing knowledge gaps for successful implementation.
A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems

The chef-based optimization algorithm (CBOA) is a human-based method inspired by the relationship between culinary students and chef instructors. The original CBOA does not have a process that separately controls population generation and updating. For this reason, in this study, a novel improved CBOA with diffusion process (DP) algorithm with the Gaussian walk (CBOADP) is proposed. In this way, it is aimed to transfer promising members to the next iteration. In the original CBOA algorithm, the selection of the chefs is performed randomly. The oppositional-based learning (OBL) approach aims to find the current best chef by updating the chef from the chef candidates selected from opposite regions. Because the selection of the chef plays an important role in the selection of students. To develop the method with optimal parameters, parameter analysis is performed as the DP’s walk and maximum diffusion parameters. The new method’s performance is evaluated through the CEC2019 and CEC2022 test suites and traditional and advanced methods. Results for real design problems are also reported. All results are supported by the Wilcoxon sign and Friedman rank statistical tests. As a result, in general, the best convergence has been achieved with CBOADP in about 75% of the benchmark test data, and statistically, it has first rank among other methods on average. In addition, CBOADP reached the best fitness value in five of the six compared engineering problems. Accordingly, it is seen that DP and OBL approaches contribute positively to the performance of CBOA and show a successful convergence trend among other compared methods.
A differential evolution algorithm based on accompanying population and piecewise evolution strategy

Differential evolution (DE) is a simple and effective stochastic search algorithm, but its convergence speed and population diversity often decline catastrophically with the evolution process. In this paper, a differential evolution algorithm based on accompanying population and piecewise evolution strategy (APPDE) is proposed. The accompanying population is used to store suboptimal solutions, and its initialization, reinitialization and renewal mechanisms are designed to maintain the characteristics of suboptimal solutions and enhance the population diversity. The mutation operators are improved based on the accompanying population to balance the exploration and exploitation ability. In view of the phenomenon that the evolution speed slows down or even stagnates, the mutation strategies and control parameters are optimized by combining with piecewise evolution. The performance of APPDE is evaluated on CEC2014, CEC2015 and CEC2017 benchmark problem suites, and compared with the state-of-the-art optimization algorithms. The results show that APPDE has better performance than the competitive algorithms.
Machine learning for forecasting a photovoltaic (PV) generation system

To mitigate the carbon print of buildings, they should have on-site renewable energy generation systems to supply energy for the buildings without relying on the national grid. Renewable generation sources rely on weather conditions and are therefore difficult to rely on as the only source of energy. Photovoltaic (PV) is forecasted through machine learning algorithms (MLA), but different methods have varied accuracy and have different training requirements such as more inputs or more data in general. No previous research has concluded an optimal MLA but to better apply them to PV systems, this must be established. To conclude an optimal MLA for a particular application, the dataset and required outputs must be determined, and how they affect the performance of the algorithm must be evaluated. The aim of this work is to compare benchmark MLA's through accuracy and usability for an operational University campus located in central Manchester, in the north of England. The MLA's including random forest (RF), neural networks (NN), support vector machines (SVM), and linear regression (LR) have been employed to forecast the PV system. If the power output of the renewables is accurately forecasted, a building management system (BMS) can be equipped to optimise on-site renewable energy generation. To accomplish this, sixty-four MLA models are created in total for forecasting at multiple horizons and dataset sizes which are validated against real-time data. Results in this work revealed that the RF algorithms have the lowest average error of the multiple tests at 32 root mean squared error (RMSE), whereas SVM, LR, and NN showed at 32.3 RMSE, 36.5 RMSE, and 38.9 RMSE respectively. Errors between forecasted and actual results are recorded in RMSE whereas changes in error are shown in mean actual percentage error (MAPE) to show the changes with respect to the original value. No MLA outperforms all others for accuracy and for requiring less data. No previous research is conducted to evaluate the performance of various MLA PV forecasting models through various sized data sets with critical analysis on the results. The comparison of benchmark algorithms when forecasting the PV generation of a local system allows the critical analysis of the models' accuracy and surrounding characteristics.
Foreign direct investment and industrialization in Tanzania admixture time series forecast analysis 1960 - 2020

ABSTRACTABSTRACTThis paper examines and forecasts the impact of foreign direct investment (FDI) on industrialization and industrial performance in Tanzania by using World Bank data spanning 1960 to 2020. The admixture time series analysis of Vector Autoregressive (VAR) and Vector Error Correction Model (VECM) has been extensively explored in order to provide accurate estimation. The need to examine FDI inflows is enormously based on domestic macroeconomic parameters that are stuck in many developing countries, including Tanzania, implying that FDI is necessary for growth and development now and in the future. According to the findings, FDI granger causes industrialization, and the more the sector thrives, the more granger causes FDI inflow. In the long run, FDI has a significant impact on Tanzanian industrialization growth, whereas the exchange rate (EXR) has a significant impact on industrialization growth in the short run. The study recommends the Bank of Tanzania to take appropriate measures to control poor-performing economic parameters such as the exchange rate, inflation, and the improvement of the money market in order to enhance capital availability and accessibility.KEYWORDS: Industrializationforeign direct investmentvector error correction modelTanzaniavector autoregressiveJEL CLASSIFICATION: E630F43F47O43O550 Disclosure statementNo potential conflict of interest was reported by the authors.
Waterwheel Plant Algorithm: A Novel Metaheuristic Optimization Method

Attempting to address optimization problems in various scientific disciplines is a fundamental and significant difficulty requiring optimization. This study presents the waterwheel plant technique (WWPA), a novel stochastic optimization technique motivated by natural systems. The proposed WWPA’s basic concept is based on modeling the waterwheel plant’s natural behavior while on a hunting expedition. To find prey, WWPA uses plants as search agents. We present WWPA’s mathematical model for use in addressing optimization problems. Twenty-three objective functions of varying unimodal and multimodal types were used to assess WWPA’s performance. The results of optimizing unimodal functions demonstrate WWPA’s strong exploitation ability to get close to the optimal solution, while the results of optimizing multimodal functions show WWPA’s strong exploration ability to zero in on the major optimal region of the search space. Three engineering design problems were also used to gauge WWPA’s potential for improving practical programs. The effectiveness of WWPA in optimization was evaluated by comparing its results with those of seven widely used metaheuristic algorithms. When compared with eight competing algorithms, the simulation results and analyses demonstrate that WWPA outperformed them by finding a more proportionate balance between exploration and exploitation.
Entangling microwaves with light

Entanglement is a genuine quantum mechanical property and the key resource in currently developed quantum technologies. Sharing this fragile property between superconducting microwave circuits and optical or atomic systems would enable new functionalities but has been hindered by the tremendous energy mismatch of $\sim10^5$ and the resulting mutually imposed loss and noise. In this work we create and verify entanglement between microwave and optical fields in a millikelvin environment. Using an optically pulsed superconducting electro-optical device, we deterministically prepare an itinerant microwave-optical state that is squeezed by $0.72^{+0.31}_{-0.25}$\,dB and violates the Duan-Simon separability criterion by $>5$ standard deviations. This establishes the long-sought non-classical correlations between superconducting circuits and telecom wavelength light with wide-ranging implications for hybrid quantum networks in the context of modularization, scaling, sensing and cross-platform verification.
Chaotic Sand Cat Swarm Optimization

In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO’s core search process to improve global search performance and convergence behavior. Thus, randomness in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical and dynamic properties. In addition to these advantages, low search consistency, local optimum trap, inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO, several chaotic maps are implemented for more efficient behavior in the exploration and exploitation phases. Experiments are conducted on a wide variety of well-known test functions to increase the reliability of the results, as well as real-world problems. In this study, the proposed algorithm was applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results.
A state-of-the-art survey of artificial neural networks for Whole-slide Image analysis: From popular Convolutional Neural Networks to potential visual transformers

In recent years, with the advancement of computer-aided diagnosis (CAD) technology and whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the diagnosis and analysis of diseases. To increase the objectivity and accuracy of pathologists' work, artificial neural network (ANN) methods have been generally needed in the segmentation, classification, and detection of histopathological WSI. However, the existing review papers only focus on equipment hardware, development status and trends, and do not summarize the art neural network used for full-slide image analysis in detail. In this paper, WSI analysis methods based on ANN are reviewed. Firstly, the development status of WSI and ANN methods is introduced. Secondly, we summarize the common ANN methods. Next, we discuss publicly available WSI datasets and evaluation metrics. These ANN architectures for WSI processing are divided into classical neural networks and deep neural networks (DNNs) and then analyzed. Finally, the application prospect of the analytical method in this field is discussed. The important potential method is Visual Transformers.
Embedding theory of reservoir computing and reducing reservoir network using time delays

The dynamics of reservoir computing (RC), a compact recurrent neural network, is validated as a higher-dimensional embedding of the input nonlinear dynamics. Based on this rigorous validation, a delayed RC is established with a significantly reduced network size and a promoted memory capacity, which can achieve dynamics reconstruction even in the reservoir with a single neuron.
Fetal Health State Detection Using Interval Type-2 Fuzzy Neural Networks

Diagnosis of fetal health is a difficult process that depends on various input factors. Depending on the values or the interval of values of these input symptoms, the detection of fetal health status is implemented. Sometimes it is difficult to determine the exact values of the intervals for diagnosing the diseases and there may always be disagreement between the expert doctors. As a result, the diagnosis of diseases is often carried out in uncertain conditions and can sometimes cause undesirable errors. Therefore, the vague nature of diseases and incomplete patient data can lead to uncertain decisions. One of the effective approaches to solve such kind of problem is the use of fuzzy logic in the construction of the diagnostic system. This paper proposes a type-2 fuzzy neural system (T2-FNN) for the detection of fetal health status. The structure and design algorithms of the T2-FNN system are presented. Cardiotocography, which provides information about the fetal heart rate and uterine contractions, is employed for monitoring fetal status. Using measured statistical data, the design of the system is implemented. Comparisons of various models are presented to prove the effectiveness of the proposed system. The system can be utilized in clinical information systems to obtain valuable information about fetal health status.
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks

The last decade of machine learning has seen drastic increases in scale and capabilities. Deep neural networks (DNNs) are increasingly being deployed in the real world. However, they are difficult to analyze, raising concerns about using them without a rigorous understanding of how they function. Effective tools for interpreting them will be important for building more trustworthy AI by helping to identify problems, fix bugs, and improve basic understanding. In particular, “inner” interpretability techniques, which focus on explaining the internal components of DNNs, are well-suited for developing a mechanistic understanding, guiding manual modifications, and reverse engineering solutions. Much recent work has focused on DNN interpretability, and rapid progress has thus far made a thorough systematization of methods difficult. In this survey, we review over 300 works with a focus on inner interpretability tools. We introduce a taxonomy that classifies methods by what part of the network they help to explain (weights, neurons, subnetworks, or latent representations) and whether they are implemented during (intrinsic) or after (post hoc) training. To our knowledge, we are also the first to survey a number of connections between interpretability research and work in adversarial robustness, continual learning, modularity, network compression, and studying the human visual system. We discuss key challenges and argue that the status quo in interpretability research is largely unproductive. Finally, we highlight the importance of future work that emphasizes diagnostics, debugging, adversaries, and benchmarking in order to make interpretability tools more useful to engineers in practical applications.
Fourier-based type-2 fuzzy neural network: Simple and effective for high dimensional problems

The main contribution of this study is to introduce a simple and effective deep learning Fourier-based type-2 fuzzy neural network for high-dimensional problems. The rules are directly constructed by fast Fourier transformation. The input matrix/vector is segmented, and each segment represents a fuzzy rule. The upper/lower bounds of rule firings are obtained by the Fourier transformation approach. The output is computed by a simple type-reduction method. All antecedent and consequent parameters are optimized by simple gradient descent and fuzzy correntropy-based extended Kalman filter. The kernel size of a conventional correntropy-based filters is determined by a fuzzy system. The convergence of the learning method is proved by the Lyapunov method. The effectiveness of the suggested approach is verified by the face recognition problem (1024 input variables), English handwriting digit recognition (1024 input variables), and modeling problem with real-world data set (32 input variables). The simulations and comparisons demonstrate the superiority of the introduced scheme.
PANGAEA - Data Publisher for Earth &amp; Environmental Science

The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system.
Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review

Oral cancer (OC) is one of the most common forms of head and neck cancer and continues to have the lowest survival rates worldwide, even with advancements in research and therapy. The prognosis of OC has not significantly improved in recent years, presenting a persistent challenge in the biomedical field. In the field of oncology, artificial intelligence (AI) has seen rapid development, with notable successes being reported in recent times. This systematic review aimed to critically appraise the available evidence regarding the utilization of AI in the diagnosis, classification, and prediction of oral cancer (OC) using histopathological images. An electronic search of several databases, including PubMed, Scopus, Embase, the Cochrane Library, Web of Science, Google Scholar, and the Saudi Digital Library, was conducted for articles published between January 2000 and January 2023. Nineteen articles that met the inclusion criteria were then subjected to critical analysis utilizing QUADAS-2, and the certainty of the evidence was assessed using the GRADE approach. AI models have been widely applied in diagnosing oral cancer, differentiating normal and malignant regions, predicting the survival of OC patients, and grading OC. The AI models used in these studies displayed an accuracy in a range from 89.47% to 100%, sensitivity from 97.76% to 99.26%, and specificity ranging from 92% to 99.42%. The models' abilities to diagnose, classify, and predict the occurrence of OC outperform existing clinical approaches. This demonstrates the potential for AI to deliver a superior level of precision and accuracy, helping pathologists significantly improve their diagnostic outcomes and reduce the probability of errors. Considering these advantages, regulatory bodies and policymakers should expedite the process of approval and marketing of these products for application in clinical scenarios.
STC-YOLO: Small Object Detection Network for Traffic Signs in Complex Environments

The detection of traffic signs is easily affected by changes in the weather, partial occlusion, and light intensity, which increases the number of potential safety hazards in practical applications of autonomous driving. To address this issue, a new traffic sign dataset, namely the enhanced Tsinghua-Tencent 100K (TT100K) dataset, was constructed, which includes the number of difficult samples generated using various data augmentation strategies such as fog, snow, noise, occlusion, and blur. Meanwhile, a small traffic sign detection network for complex environments based on the framework of YOLOv5 (STC-YOLO) was constructed to be suitable for complex scenes. In this network, the down-sampling multiple was adjusted, and a small object detection layer was adopted to obtain and transmit richer and more discriminative small object features. Then, a feature extraction module combining a convolutional neural network (CNN) and multi-head attention was designed to break the limitations of ordinary convolution extraction to obtain a larger receptive field. Finally, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to make up for the sensitivity of the intersection over union (IoU) loss to the location deviation of tiny objects in the regression loss function. A more accurate size of the anchor boxes for small objects was achieved using the K-means++ clustering algorithm. Experiments on 45 types of sign detection results on the enhanced TT100K dataset showed that the STC-YOLO algorithm outperformed YOLOv5 by 9.3% in the mean average precision (mAP), and the performance of STC-YOLO was comparable with that of the state-of-the-art methods on the public TT100K dataset and CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB2021) dataset.