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The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.

Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.

Research indicates that integral control parameters (K_i) affect outputs substantially more than proportional control parameters (K_p). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.

This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

The primary aim of this paper is to present a novel design approach for a ring voltage-controlled oscillator (VCO) suitable for L-band applications, whose oscillation frequency is less sensitive to power supply variations. In a few decades, with the advancement of modern wireless communication equipment, there has been an increasing demand for low-power and robust communication systems for longer battery life. A sudden drop in power significantly affects the performance of the VCO. Supply insensitive circuit design is the backbone of uninterrupted VCO performance. Because of their important roles in a variety of applications, VCOs and phase locked loops (PLLs) have been the subject of significant research for decades. For a few decades, the VCO has been one of the major components used to provide a local frequency signal to the PLL.

First, this paper chose to present recent developments on implemented techniques of ring VCO design for various applications. A complementary metal oxide semiconductor (CMOS)-based supply compensation technique is presented, which aims to reduce the change in oscillation frequency with the supply. The proposed circuit is designed and simulated on Cadence Virtuoso in 0.18 µm CMOS process under 1.8 V power supply. Active differential configuration with a cross-coupled NMOS structure is designed, which eliminates losses and negates supply noise. The proposed VCO is designed for excellent performance in many areas, including the L-band microwave frequency range, supply sensitivity, occupied area, power consumption and phase noise.

This work provides the complete design aspect of a novel ring VCO design for the L-band frequency range, low phase noise, low occupied area and low power applications. The maximum value of the supply sensitivity for the proposed ring VCO is 1.31, which is achieved by changing the VDD by ±0.5%. A tuning frequency range of 1.47–1.81 GHz is achieved, which falls within the L-band frequency range. This frequency range is achieved by varying the control voltage from 0.0 to 0.8 V, which shows that the proposed ring VCO is also suitable for low voltage regions. The total power consumed by the proposed ring VCO is 14.70 mW, a remarkably low value using this large transistor count. The achievable value of phase noise is −88.76 dBc/Hz @ 1 MHz offset frequency, which is a relatively small value. The performance of the proposed ring VCO is also evaluated by the figure of merit, achieving −163.13 dBc/Hz, which assures the specificity of the proposed design. The process and temperature variation simulations also validate the proposed design. The proposed oscillator occupied an extremely small area of only 0.00019 mm² compared to contemporary designs.

The proposed CMOS-based supply compensation method is a unique design with the size and other parameters of the components used. All the data and results obtained show its originality in comparison with other designs. The obtained results are preserved to the fullest extent.

By introducing Stacking algorithm to solve the underfitting problem caused by insufficient data in traditional machine learning, this paper provides a new solution to the cold start problem of entrepreneurial borrowing risk control.

The authors introduce semi-supervised learning and integrated learning into the field of migration learning, and innovatively propose the Stacking model migration learning, which can independently train models on entrepreneurial borrowing credit data, and then use the migration strategy itself as the learning object, and use the Stacking algorithm to combine the prediction results of the source domain model and the target domain model.

The effectiveness of the two migration learning models is evaluated with real data from an entrepreneurial borrowing. The algorithmic performance of the Stacking-based model migration learning is further improved compared to the benchmark model without migration learning techniques, with the model area under curve value rising to 0.8. Comparing the two migration learning models reveals that the model-based migration learning approach performs better. The reason for this is that the sample-based migration learning approach only eliminates the noisy samples that are relatively less similar to the entrepreneurial borrowing data. However, the calculation of similarity and the weighing of similarity are subjective, and there is no unified judgment standard and operation method, so there is no guarantee that the retained traditional credit samples have the same sample distribution and feature structure as the entrepreneurial borrowing data.

From a practical standpoint, on the one hand, it provides a new solution to the cold start problem of entrepreneurial borrowing risk control. The small number of labeled high-quality samples cannot support the learning and deployment of big data risk control models, which is the cold start problem of the entrepreneurial borrowing risk control system. By extending the training sample set with auxiliary domain data through suitable migration learning methods, the prediction performance of the model can be improved to a certain extent and more generalized laws can be learned.

This paper introduces the thought method of migration learning to the entrepreneurial borrowing scenario, provides a new solution to the cold start problem of the entrepreneurial borrowing risk control system and verifies the feasibility and effectiveness of the migration learning method applied in the risk control field through empirical data.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

This paper aims to present the simulation, fabrication and testing of a novel ultra-wide band (UWB) band-pass filters (BPFs) with better transmission and rejection characteristics on a low-loss Taconic substrate and analyze using the coupled theory of resonators for UWB range covering L, S, C and X bands for radars, global positioning system (GPS) and satellite communication applications.

The filter is designed with a bent coupled transmission line on the top copper layer. Defected ground structures (DGSs) like complementary split ring resonators (CSRRs), V-shaped resonators, rectangular slots and quad circle slots (positioned inwards and outwards) are etched in the ground layer of the filter. The circular orientation of V-shaped resonators adds compactness when linearly placed. By arranging the quad circle slots outwards and inwards at the corner and core of the ground plane, respectively, two filters (Filters I and II) are designed, fabricated and measured. These two filters feature a quasi-elliptic response with transmission zeros (TZs) on either side of the bandpass response, making it highly selective and reflection poles (RPs), resulting in a low-loss filter response. The transmission line model and coupled line theory are implemented to analyze the proposed filters.

Two filters by placing the quad circle slots outwards (Filter I) and inwards (Filter II) were designed, fabricated and tested. The fabricated model (Filter I) provides transmission with a maximum insertion loss of 2.65 dB from 1.5 GHz to 9.2 GHz. Four TZs and five RPs are observed in the frequency response. The lower and upper stopband band width (BW) of the measured Filter I are 1.2 GHz and 5.5 GHz of upper stopband BW with rejection level greater than 10 dB, respectively. Filter II (inward quad circle slots) operates from 1.4 GHz to 9.05 GHz with 1.65 dB maximum insertion loss inside the passband with four TZs and four RPs, which, in turn, enhances the filter characteristics in terms of selectivity, flatness and stopband. Moreover, 1 GHz BW of lower and upper stopbands are observed. Thus, the fabricated filters (Filters I and II) are therefore evaluated, and the outcomes show good agreement with the electromagnetic simulation response.

The limitation of this work is the back radiation caused by DGS, which can be eradicated by placing the filter in the cavity and retaining its performance.

The proposed UWB BPFs with novel resonators find their role in the UWB range covering L, S, C and X bands for radars, GPS and satellite communication applications.

To the best of the authors’ knowledge, for the first time, the authors develop a compact UWB BPFs (Filters I and II) with BW greater than 7.5 GHz by combining reformed coupled lines and DGS resonators (CSRRs, V-shaped resonators [modified hairpin resonators], rectangular slots and quad circle slots [inwards and outwards]) for radars, GPS and satellite communication applications.

This study aims to assess the technological capability of Chinese internet platforms (BAT: Baidu, Alibaba, Tencent) compared to US ones (GAFA: Google, Amazon, Facebook, Apple). More specifically, this study explores Baidu’s technological catching-up process with Google by analyzing their patent textual information.

The authors retrieved 26,383 Google patents and 6,695 Baidu patents from PATSTAT 2019 Spring version. The collected patent documents were vectorized using the Word2Vec model first, and then K-means clustering was applied to visualize the technological space of two firms. Finally, novel indicators were proposed to capture the technological catching-up process between Baidu and Google.

The results show that Baidu follows a trend of US rather than Chinese technology which suggests Baidu is aggressively seeking to catch up with US players in the process of its technological development. At the same time, the impact index of Baidu patents increases over time, reflecting its upgrading of technological competitiveness.

This study proposed a new method to analyze technology mapping and evolution based on patent text information. As both US and China are crucial players in the internet industry, it is vital for policymakers in third countries to understand the technological capacity and competitiveness of both countries to develop strategic partnerships effectively.

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

Referred data set produces reliable information about the network flows and common attacks meeting with real-world criteria. Accordingly, this study aims to focus on the use of imbalanced intrusion detection benchmark knowledge discovery in database (KDD) data set. KDD data set is most preferably used by many researchers for experimentation and analysis. The proposed algorithm improvised random forest classification with error tuning factors (IRFCETF) deals with experimentation on KDD data set and evaluates the performance of a complete set of network traffic features through IRFCETF.

In the current era of applications, the attention of researchers is immersed by a diverse number of existing time applications that deals with imbalanced data classification (ImDC). Real-time application areas, artificial intelligence (AI), Industrial Internet of Things (IIoT), etc. are dealing ImDC undergo with diverted classification performance due to skewed data distribution (SkDD). There are numerous application areas that deal with SkDD. Many of the data applications in AI and IIoT face the diverted data classification rate in SkDD. In recent advancements, there is an exponential expansion in the volume of computer network data and related application developments. Intrusion detection is one of the demanding applications of ImDC. The proposed study focusses on imbalanced intrusion benchmark data set, KDD data set and other benchmark data set with the proposed IRFCETF approach. IRFCETF justifies the enriched classification performance on imbalanced data set over the existing approach. The purpose of this work is to review imbalanced data applications in numerous application areas including AI and IIoT and tuning the performance with respect to principal component analysis. This study also focusses on the out-of-bag error performance-tuning factor.

Experimental results on KDD data set shows that proposed algorithm gives enriched performance. For referred intrusion detection data set, IRFCETF classification accuracy is 99.57% and error rate is 0.43%.

This research work extended for further improvements in classification techniques with multiple correspondence analysis (MCA); hierarchical MCA can be focussed with the use of classification models for wide range of skewed data sets.

The metrics enhancement is measurable and helpful in dealing with intrusion detection systems–related imbalanced applications in current application domains such as security, AI and IIoT digitization. Analytical results show improvised metrics of the proposed approach than other traditional machine learning algorithms. Thus, error-tuning parameter creates a measurable impact on classification accuracy is justified with the proposed IRFCETF.

Proposed algorithm is useful in numerous IIoT applications such as health care, machinery automation etc.

This research work addressed classification metric enhancement approach IRFCETF. The proposed method yields a test set categorization for each case with error reduction mechanism.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

Key to transnational higher education (HE) cooperation is building trust to allow for seamless recognition of studies. Building on the Tuning Educational Structures initiative (2001) and lessons learnt from the Organisation for Economic Cooperation and Development (OECD)-Assessment of Learning Outcomes in Higher Education (AHELO) feasibility study, this paper offers a sophisticated approach developed by the European Union (EU)-co-financed project Measuring and Comparing Achievements of Learning Outcomes in Europe (CALOHEE). These evidence the quality and relevance of learning by applying transparent and reliable indicators at the overarching and disciplinary levels. The model results allow for transnational diagnostic assessments to identify the strength and weaknesses of degree programmes.

The materials presented have been developed from 2016 to 2023, applying a bottom-up approach involving approximately 150 academics from 20+ European countries, reflecting the full spectrum of academic fields. Based on intensive face-to-face debate and consultation of stakeholders and anchored in academic literature and wide experience.

As a result, general (overarching) state-of-the-art reference frameworks have been prepared for the associated degree, bachelor, master and doctorate, as well as aligned qualifications reference frameworks and more detailed learning outcomes/assessment frameworks for 11 subject areas, offering a sound basis for quality assurance. As a follow-up, actual assessment formats for five academic fields have been developed to allow for measuring the actual level of learning at the institutional level from a comparative perspective.

Frameworks as well as assessment models and items are highly innovative, content-wise as in the strategy of development, involving renown academics finding common ground. Its value is not limited to Europe but has global significance. The model developed, is also relevant for micro-credentials in defining levels of mastery.