Demand For Quality Research Articles

Integrating AI in testing automation: Enhancing test coverage and predictive analysis for improved software quality

This research explores the integration of Artificial Intelligence (AI) in testing automation, focusing on its ability to enhance test coverage and enable predictive analysis for improved software quality. As software systems become increasingly complex, traditional testing methods often struggle to meet quality demands. This study evaluates various AI techniques, including machine learning and natural language processing, and their applications in generating test cases, optimizing testing processes, and predicting defects. Through empirical case studies from diverse organizations, we demonstrate significant improvements in test coverage and defect detection rates following AI implementation. The findings highlight the efficiency gains and quality enhancements achieved through AI-driven testing, while also addressing challenges such as data dependency, complexity of implementation, and the need for skilled personnel. This research contributes to the understanding of AI's role in software testing and encourages organizations to adopt these technologies for better quality assurance and faster development cycles.

Solar PV powered DC charger for electric vehicles with reconfigurable power electronic interface

ABSTRACT Global expansion of the EV market has led to widespread DC charging infrastructure. Consumers prefer DC charging over AC to reduce charging time per kilometer. However, grid-connected DC chargers can cause issues such as grid instability, power quality problems, demand imbalance, and conversion losses. This paper proposes an off-grid photovoltaic (PV) fed DC charger for EVs along with a battery energy storage system (BESS) to mitigate these challenges. The work aims to increase the use of clean energy for EV charging, to ensure uninterrupted EV charging with minimum power conversion stages, and to improve PV utilization. BESS serves as a backup power source that charges or discharges based on PV availability. Additionally, the system powers the non-critical adjustable loads of domestic/industrial sites with the available PV power when both EV and BESS are fully charged. The system can be reconfigured to operate in six distinct modes using two DC-DC converters and five relays. Among six modes, four modes operate in single-stage power conversion. A buck converter is used to extract power from PV. A bidirectional buck-boost converter is used to charge and discharge the BESS. The proposed system is validated through hardware prototype for both steady-state and transient conditions.

Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction

The advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its “understand before transmit” approach, has emerged as a pivotal technology driving the progress of 6G due to its ability to enhance communication efficiency and quality. The Wireless Image Transmission Transformer (WITT) model, which operates as a semantic communication system leveraging vision transformer technology for the transmission of semantic images, has shown efficacy in transmitting input images through processes of feature extraction and channel adaptation. This study introduces an advanced channel adaptive module that is informed by deep learning methodologies and the adaptive modulation principles of the Variational Information Bottleneck (VIB). This innovation enhances the original WITT model, resulting in the development of the Adaptive Wireless Image Transmission Transformer (ADWITT) architecture. Comprehensive experimental results have unequivocally shown that the transmission performance of the ADWITT architecture substantially surpasses that of the conventional WITT (Wavelet Image Transmission Technique) model, particularly in scenarios characterized by harsh and detrimental channel conditions. These findings underscore the robustness and adaptability of the ADWITT approach, which is poised to improve the field of image transmission by offering superior performance and resilience in environments where traditional methods falter.

Telescope Alignment Method Using a Modified Stochastic Parallel Gradient Descent Algorithm

To satisfy the demands of high image quality and resolutions, telescope alignment is indispensable. In this paper, a wavefront sensorless method based on a modified stochastic parallel gradient descent algorithm (SPGD) called the adaptive moment estimation SPGD (Adam SPGD) algorithm is proposed. Simulations are carried out using a four-mirror telescope, whose aperture is 6 m and fields of view are Φ2°. Three misalignments are shown as examples. Positions of the secondary mirror and third mirror are employed to compensate aberrations. The results show that merit functions and energy distributions of corrected images match with the designed ones. The mean RMS of residual wavefront errors is smaller than λ/14 (λ = 0.5 μm), indicating that the misalignments are well compensated. The results verify the effectiveness of our method.

Dynamic state awareness for distribution network based on robust adaptive cubature kalman filter

With the rapid development of the economy and society, the demand for power quality is constantly increasing. As a crucial part of grid situational awareness, distribution network state estimation plays a vital role in providing critical data support for other advanced applications, which is significant for ensuring the safe and reliable operation of the distribution network. Therefore, this paper proposes a dynamic state awareness method for distribution networks based on the robust adaptive cubature Kalman filter (RACKF). The proposed solution, grounded on the core computational concept of the cubature Kalman filter (CKF), constructs a robust noise statistical estimator (NSE) composed of a biased NSE and an unbiased NSE to adapt to the unknown and time-varying process noise parameters in the dynamic state estimation process. The proposed solution can ensure that the calculated process noise parameters always meet the constraints and guarantee the robustness of the algorithm. In addition, an estimation strategy for the fusion of multi-time scale measurement data is developed according to the RACKF-based dynamic state estimation features in order to realize system state corrections and updates. The results of simulation experiments and comparisons with traditional CKF methods demonstrate the accuracy and superiority of the proposed solution.

A multi-label text classifier: application on an Italian public tender procedure, project ISCOL@

The main means of communication during the pre-design phase is natural language. Effective communication during the pre-design phase through text documents and reports is essential to the success of a design and construction project. The study develops and evaluates a Natural Language Processing (NLP) tool called ArchiBERTo to process textual data related to design tender documents in the Italian public procurement process. Documenti di Indirizzo alla Progettazione (DIPs) are key documents, as they outline the demands, needs, and objectives of the public appointing party. ArchiBERTo is developed to process and convert DIP quality objective sentences into a hierarchy of objectives and criteria. The performances are evaluated by comparing the tool’s rankings with those provided by a group of domain experts. The results demonstrate ArchiBERTo's capability to reflect the collective ability of a panel of experts and to properly reflect the different contents of the DIP in the objectives hierarchy. The proposed system aims to address the issue of information asymmetry and potential misunderstandings, or varying interpretations, among the key actors of the Italian tendering procedure, namely the public appointing party, the design teams, and the external committee, regarding the relative importance of quality objectives and evaluation criteria. The utilization of the NLP systems ArchiBERTo to establish a shared hierarchy of objectives is expected to enhance communication and promote consensus during the pre-design phase. The minimization of the consensus issue among the actors can have a positive impact on the overall quality of the design proposals and facilitate the evaluation process conducted by the external committee, bridging the gap between expected and actual quality, ensuring that design proposals align with the quality objectives and demands of the public actor.

Consumers’ Perceptions and Behaviors Regarding Honey Purchases and Expectations on Traceability and Sustainability in Italy

Traceability is a cornerstone of sustainable honey production and consumption. Honey fraud and a lack of traceability have been recently highlighted by the European Commission. Innovative systems aimed at guaranteeing food safety ’from farm to fork’ and improved controls are highly recommended. Within the framework of the BPRACTICES project, part of the European Union’s Horizon 2020 research and innovation program, and the ERA-Net SusAn initiative—focused on Sustainable Animal Production Systems—an advanced traceability system has been developed. This system utilizes QR code and radio-frequency identification (RFID) technology, along with a user-friendly web application, to facilitate direct interactions between producers and consumers. Despite existing research, studies on the information needs of Italian consumers regarding honey and its traceability remain limited. Understanding these needs is vital for creating effective communication strategies that enhance consumer satisfaction and trust. This study aims to identify the needs of Italian consumers’ honey during the purchasing and consumption decisions. To explore consumer perceptions, behaviors, expectations, and needs regarding honey, we employed diverse social research methodologies, including a quantitative online survey, paper-and-pencil interviews, and focus groups. The results of this study indicate a robust demand for more information on honey’s origin, production processes, and beekeeping practices, aligning with the recent EU Directive 2024/1438, which mandates clear labeling. Italian consumers would be willing to pay a premium for honey that offers detailed information about production practices and transparency. The positive reception of QR code technology by consumers suggests a growing openness to digital tools that enhance transparency and access to information. Ultimately, this research emphasizes the need for the beekeeping sector to adopt sustainable practices, improve traceability systems, and actively engage with consumers to foster trust and ensure long-term viability in the honey market. By addressing these information needs, the sector can align itself with increasing consumer demand for quality, sustainability, and transparency.

The Optimal Drought Hardening Intensity and Salinity Level Combination for Tomato (Solanum lycopersicum L.) Cultivation under High-Yield, High-Quality and Water-Saving Multi-Objective Demands.

The extreme weather and the deteriorating water environment have exacerbated the crisis of freshwater resource insufficiency. Many studies have shown that salty water could replace freshwater to partly meet the water demand of plants. To study the effects of early-stage drought hardening and late-stage salt stress on tomatoes (Solanum lycopersicum L.), we conducted a 2-year pot experiment. Based on the multi-objective demands of high yield, high quality, and water saving, yield indicators, quality indicators, and a water-saving indicator were selected as evaluation indicators. Three irrigation levels (W1: 85% field capacity (FC), W2: 70% FC, W3: 55% FC) and three salinity levels (S2: 2 g/L, S4: 4 g/L, S6: 6 g/L) were set as nine treatments. In addition, a control treatment (CK: W1, 0 g/L) was added. Each treatment was evaluated and scored by principal component analysis. The results for 2022 and 2023 found the highest scores for CK, W2S2, W3S2 and CK, W2S4, W2S2, respectively. Based on response surface methodology, we constructed composite models of multi-objective demands, whose results indicated that 66-72% FC and 2 g/L salinity were considered the appropriate water-salt combinations for practical production. This paper will be beneficial for maintaining high yield and high quality in tomato production using salty water irrigation.

CARCASS CHARACTERISTICS OF NATIVE CHICKEN VARIETIES OF INDIA – REVIEW

The total meat production in India is 9.29 million out of which 4.78 million tonnes is from poultry and the growth of poultry meat sector has increased by 6.86 % over previous year. The growth rate of backyard poultry population from 2012 to 2019 (45.79 %) is ten times higher than when compared to commercial poultry (4.50 %) (BAHS, 2022). The native chickens are slowly growing in nature and are preferred for better flavor and for the belief that natural, less intensive management systems provide desi birds with higher welfare levels, resulting in much better product quality and great demand. The meat from native chicken is more delicious and provides nutritional security and economic support for the rural and most suited for backyard system of rearing. The aim is to study the carcass traits of available native chicken in India and compare it with native chicken at Hosur. The carcass characteristic of the native chicken variety at Hosur was evaluated by sacrificing 25 birds of each sex at 16 weeks of age. The observed results were compared with the performances of other varieties of native chicken in India with respect to the parameters like New York dressing yield, eviscerated yield, Ready to cook yield, cut-up parts yield, Meat bone ratio and Giblets yield at each sex of the bird at different age of slaughter especially at 16 weeks of age. It helps to identify the variety of native chicken which had good carcass traits and could be utilized for table delicacy.

Systematic Analysis of Packaging Production in the Electric Motors Industry: A Multi-Criteria Approach through the SAPEVO-M Method

Market competitiveness drives the electric motors industry, which in turn necessitates the selection of optimal production scenarios, particularly in the context of packaging. This is crucial for maintaining competitiveness and meeting the rigorous quality and logistical demands that are characteristic of this industry. This paper presents a systematic analysis of the packaging production chain for electric motors, employing the SAPEVO-M method as a decision aid tool. The study examines various strategic options, including outsourcing and internalizing processes, with a particular focus on their impacts on logistics, quality control, and overall supply-chain efficiency. The research conducts a comprehensive evaluation of these strategies to ascertain the most effective approach for managing the complexities of packaging production. The SAPEVO-M method facilitated a structured decision-making process, allowing for the aggregation and prioritization of diverse criteria such as cost, quality, flexibility, environmental impact, and supply risk. A sensitivity analysis was performed to validate the robustness of the decision-making outcomes under varying alternatives. The findings highlight the benefits of internalizing certain processes, particularly the assembly (with a score of 43.27%), to gain direct control over production variables, leading to enhanced operational efficiency and product competitiveness. This paper contributes to the literature by demonstrating the application of MCDA in enhancing strategic decisions within the electric motors industry, providing insights for analyzing other manufacturing factors in the improvement of supply-chain processes.

СТРАТЕГІЧНИЙ АНАЛІЗ ПІДПРИЄМСТВА АГРАРНОГО СЕКТОРУ

With the development of the economy and constant changes in the external environment, it became more difficult for Ukrainian agricultural enterprises to conduct their activities, especially under the influence of two factors, namely the pandemic and the war. The Ukrainian economy, companies and population had not yet recovered from the consequences of COVID-19 when a full-scale invasion began, which forever changed the realities of both the agricultural sector and entrepreneurship. Global climate changes, international trade conditions, technological development and demographic changes were also important external factors, which created new challenges and opportunities for agricultural enterprises. In this context, there is a need to carry out their strategic analysis to determine competitiveness and develop effective development strategies. However, at the moment there is a need for a more detailed study of issues related to the analysis of internal and external factors, their interrelationship and influence on strategic decision-making in the agricultural sector. Thus, the problem that needs to be solved is the development of an integrated methodological approach for the strategic analysis of agricultural enterprises, which would take into account various aspects of their functioning and the impact of changes in the external environment. The article is devoted to a strategic analysis of the agricultural enterprise «Dekoplant». Based on the performed PEST and SWOT analyses, main threats, opportunities, strengths, and weaknesses of both the internal and external environments were identified. Among the identified threats and weaknesses are changes in trade policy conditions, national legislation, currency exchange rate fluctuations (USD and Euro), a weak development strategy, and ineffective marketing platforms. The opportunities and strengths encompass heightened quality and service level demands, high product quality, a diverse range of products, extensive experience, and a large customer base. The article highlights the significance of strategic analysis for efficient enterprise management and gaining competitive advantages. Additionally, top companies in Ukraine's agricultural sector were investigated to determine key industry trends and competitiveness. The analytical approach enables well-founded recommendations for optimizing the strategy of the «Dekoplant» enterprise considering the identified internal and external threats.

A-247 Performance and Compatibility Assessment of Serially Diluted Microbix QAPTM Controls with Seegene NovaplexTM Assays

Abstract Background Choosing high-quality external (third-party) controls like Microbix QAPTM PROCEEDxTM Controls and REDxTM Controls is crucial during test development, validation of Laboratory Developed Tests (LDTs), performance verification, proficiency testing, instrument qualifications, operator training and R&D testing. Laboratories may prefer to include multiple positive controls at varying concentrations to achieve reliable performance across a broad range of detection concentrations. In this study, we assess the performance and compatibility of Microbix QAPTM PROCEEDxTM liquid Controls and REDxTM swabs with Seegene’s real time qPCR assays that utilize TOCE and MuDTTM technologies. TOCE and MuDTTM technologies achieve multiple reads in a single channel and are the backbone of Seegene’s wide coverage panel assays. Methods The controls below were reconstituted and/or diluted in Copan UTM and extracted on the Seegene STARlet liquid handler using Seegene’s STARmag Universal extraction kit. After extraction, PCR was run on the Bio-Rad CFX 96 Opus Dx and analyzed using the Seegene Viewer software. Results Microbix QAPsTM liquid formulations were tested at various dilutions from stock up to 1/2048 with N=5per dilution. The VP-13-01 Flu A control was detected up to a concentration of1/2048 and at this dilution had Ct values of 30 + 0.3 for the Novaplex Respiratory Panel 1A (RP1A) Assay, 28.6 + 0.3 cycles for the Seegene SARS-CoV-2/FluA/FluB/RSV (Quad) Assay, and 27.9 + 0.3 for the Respiratory Virus Master (RVM) Assay. The Microbix VP-14-01 Flu B control was detected up to 1/2048 with Ct values of 34.3 + 0.3 for the RP1A Assay, 30 + 0.5 for the Quad Assay, and 30.3 + 0.3 for the RVM Assay. The VP-07-01 RSV A control was also detected at a 1/2048 dilution with Ct values of 32.7 + 0.3 for RP1A, 30.6 + 0.6 for Quad Assay, and 29.2 + 0.4 for RVM. The VP-61-01 Trichomonas vaginalis (TV) control was detected up to a dilution of 1/128 with a Ct value of 37.3 + 0.4 when tested with the Seegene Sexually Transmitted Infection Essential (STI) Assay. The VP-02-M1 HSV1 control was detected up to a dilution of 1/128 with a Ct value of 31.3 + 0.7 for the Seegene Genital Ulcer (GU) Assay. The VP-23-M1 HSV2 control was detected p to a dilution of 1/128 with a Ct value of 31.9 + 0.5 for the GU Assay. Microbix also makes a dry swab formulation product line that we tested in 3mL of UTM. Of these we tested the RED-S-10-M1 swab covering the SARS-CoV-2, Flu A, Flu B, and RSV controls. The VP-S-12-M5 swab covering the Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), Mycoplasma genitalium (MG), and TV controls. We also tested the VP-S-03-M1 swab covering HSV1, HSV2, VZV and syphilis controls. When tested with various assays all swabs were detected at the stock formulation. Conclusions Microbix QAPsTM are inactivated whole-organism controls, mimicking patient samples, and meet the stringent demand for quality or performance assessments. Their compatibility with Seegene’s Assays over a range of dilutions and formulations makes them ideal for ensuring reliable detection across multiple diagnostic needs.

Corrosion measurement of thermally sprayed carbide coatings on stainless steel pipes

The petrochemical industries face a formidable challenge from corrosion, which leads to high maintenance costs and lost production. Traditional corrosion monitoring methods frequently fail to provide accurate and reliable results. As a result, these components must be replaced or repaired promptly to avoid increasing costs and limiting component life, which will reduce operation efficiency. Novel techniques with demands for high surface qualities in the future will be applied to develop alloys with better performance and more diverse applications. This study examines the corrosion resistance of cermet coatings, such as tungsten carbide (WC) and Chromium carbide (Cr3C2), in acidic solution by electrochemical impedance spectroscopy and electrochemical potentiodynamic polarisation. Additionally, utilizing optical microscopy techniques, the microstructural characteristics of coatings were assessed. According to the results of the Tafel polarisation test, Cr3C2 coating exhibited a lower rate of corrosion than other coatings. The Cr3C2 coated, WC coated, and uncoated samples reported corrosion rates of 0.924, 1.122, and 2.599 mmpy respectively. The corrosion resistance of Cr3C2 and WC coatings was increased by 64% and 53%, respectively, in comparison to uncoated specimens. This suggests that the coating maximizes economy, minimizes maintenance costs, extends the structure's lifespan, and reduces down the rate of corrosion.

Do Big 4 Firms Provide Higher Audit Quality in Government Audits? Evidence from Canadian Provincial Consolidated Financial Statements

SUMMARY Audit quality is influenced by both the demand for and the supply of audits. A major challenge in audit quality research involves isolating supply effects. The audits of Canadian provincial governmental entities present an appealing setting, where there is low variation in the demand for audit quality. Our analysis, employing various audit quality metrics, reveals that Big 4 firms underperform both government auditors and non-Big 4 firms in our setting. We find robust evidence that less government audit knowledge is a key channel through which Big 4 auditors underperform. Additionally, the weaker performance of Big 4 firms may be due to lower effort. Insights from interviews with government audit executives and audit committee members provide evidence supporting the quantitative results. Our results are robust to propensity score matching and to tests that address alternative explanations. Our findings have important implications for governments, audit committees, and auditors in the government sector. JEL Codes: M41; M42.

Rapid Advancements in Large Language Models for Quantitative Remote Sensing: The Case of Water Depth Inversion

This study presents a comparative analysis of two advanced AI models, ChatGPT and ERNIE, in the context of water depth inversion. Utilizing satellite spectral data and in-situ bathymetric measurements collected from Rushikonda Beach, India, we processed and analyzed the data to generate high-resolution bathymetric maps. Both models demonstrated significant accuracy, with ChatGPT slightly outperforming ERNIE in terms of mean absolute error. The study highlights the advantages of AI models, such as efficient data processing and the ability to integrate multi-modal inputs, while also discussing challenges related to data quality, interpretability, and computational demands. The findings suggest that while both models are highly effective for water depth inversion, ongoing improvements in data handling and model transparency are essential for their broader application in environmental monitoring. This research contributes to the understanding of AI capabilities in geospatial analysis and sets the stage for future enhancements in the field.

Application of Artificial Intelligence Technology in the Supervision of Customs Clearance Machine Inspection

The customs service plays a crucial role in keeping threats that endanger national security and social stability outside a country’s borders. However, illegal infiltration at the ports of entry is becoming more diverse and covert in its methods, which poses a greater challenge in effectively defending and controlling both traditional and non-traditional security threats. Additionally, with the intensification of competition in international trade and economic cooperation, and the growing demand for a better quality of life, enterprises are striving for fair competition, reduced trade costs and assurance of trade quality. This has resulted in a conflict between the goals of ‘supervision’ and ‘facilitation’. To address these challenges and improve customs operations, Customs should consider incorporating artificial intelligence (AI) and other technologies to develop ‘intelligent customs clearance and inspection’ methods. This study analyses the current deployment status and effectiveness of ‘intelligent customs inspection’ (ICI) in China, using it as a case study to highlight the need for innovative practices in customs clearance inspection supervision and to explore the future direction of ICI. The study demonstrates that the application of innovative intelligent technology practices could substantially improve the efficiency of customs clearance inspection.

Intraocular aphakia correction in patients with prior keratorefractive surgery: literature review. Part 1

Introduction. An increasing number of patients with a history of keratorefractive surgeries are presenting to ophthalmologists with complaints of vision loss due to cataracts. Treating this group poses surgeons with a range of unique challenges: high demands for vision quality, complexities in selecting the appropriate intraocular lens (IOL) power calculation formula and IOL model, target refraction, as well as the need to modify cataract extraction techniques and address specific postoperative considerations. Despite advancements in the development of new IOL designs and calculation formulas, clinical and functional outcomes in this group remain inferior to those in patients without prior keratorefractive procedures. A paradigm shift is emerging, advocating for a personalized approach in the diagnosis and management of cataracts in these patients. However, discussing all aspects within a single review proved impractical, leading us to divide it into two parts. The objective of the first part of this study is to assess the specific considerations for aphakia correction in patients who have undergone keratorefractive procedures, based on literature data, while taking into account the long-term complications of refractive surgery. Additionally, this part will address the fundamental principles of the design and functionality of pseudoaccommodating intraocular lenses (IOLs). Materials and methods. A selection of over 200 peer-reviewed publications from resources such as PubMed, eLibrary, CyberLeninka, Science Direct, and Google Scholar over the past 30 years was conducted. The first part of the review includes 49 publications. This work represents an analysis of contemporary literature, reflecting the impact of keratorefractive surgeries on the successful performance of phacoemulsification with IOL implantation. Results. The findings from the first part of the analysis indicate that a detailed medical history of previously performed keratorefractive corrections – specifically their type and potential long-term complications – play a significant role in determining the surgical treatment strategy. Standard examination methods do not always fully reflect the optical characteristics of the cornea in these patients. Extended preoperative assessments, including specialized techniques such as keratotopography and keratotomography, are crucial for identifying corneal irregularities and for the subsequent selection of the type of intraocular lens (IOL) for aphakia correction in patients who have undergone keratorefractive surgeries. Studies show high effectiveness not only in using monofocal lenses but also in the potential application of pseudoaccommodating IOLs, including those with extended depth of focus and multifocal lenses. The selection of optimal formulas for IOL calculation, as well as the clinical aspects influencing refraction in the postoperative period, will be addressed in the second part of the literature review. Conclusion. The increase in the number of refractive surgeries has led to a growing population of patients with cataracts following ametropia correction. This has spurred the development of new IOL variants with extended depth of focus. However, literature data on their effectiveness in patients who have undergone keratorefractive procedures remain limited. Multicenter prospective studies are needed to evaluate new IOL models and to determine the optimal surgical strategies for this category of patients.

Dynamic priority-based bandwidth allocation scheme for machine type communications

The burgeoning realm of Machine Type Communications (MTC) has ushered in a paradigm shift in wireless communication, necessitating innovative strategies to efficiently manage diverse application requirements. This paper presents a pioneering Dynamic Priority-based Bandwidth Allocation (DPBA) scheme tailored specifically for MTC scenarios, encompassing both MTC and Human Type Communications (HTC) coexistence. DPBA employs an adaptive framework addressing dynamic MTC traffic, optimizing resource use while meeting latency constraints and sporadic data patterns. By dynamically prioritizing MTC applications based on requirements, the scheme allocates bandwidth for reliable data delivery. DPBA extends to MTC and HTC coexistence, managing diverse quality demands. This underlines its versatility and capacity to serve distinct needs in a shared spectrum. To substantiate its performance, the DPBA scheme is rigorously evaluated against a spectrum of related allocation methods like Proportional Fairness (PF), Static Priority Scheduling (SPS), and the Machine Learning-based Scheme (MLS), DPBA excels in simulations, minimizing packet loss, latency, enhancing throughput and ensuring fairness, surpassing benchmarks. This research underscores the critical importance of tailored bandwidth allocation strategies in advancing MTC and HTC coexistent environments. With its adaptability, efficiency, and remarkable performance, the DPBA scheme emerges as a cornerstone in the trajectory of wireless communication networks, catering to the distinct requirements of both MTC and HTC applications.

Will trust move mountains? Fostering radical ideas in public organizations

Abstract Demands for greater quality of public services and enhanced efficiency have intensified changes in public organizations. Not surprisingly, these organizations are increasingly searching for new and useful ideas, including disruptive ones, to meet current demands. Whereas previous studies on team radical creativity (TRC) have focused on the influence that subordinates’ trust in the supervisor has on this type of creativity, this work innovates by testing the leader’s trust in the team as an antecedent. Drawing on Self-Determination Theory, we further add to knowledge by considering the mediating role of team-perceived organizational support for creativity and the moderating role of team collaborative climate (TCC). The research model was tested with a sample of 228 teams from public organizations with data collected from two sources at three moments in time. We found that the leader’s trust in the team has a direct positive relationship with TRC and an indirect positive relationship with creativity via team-perceived organizational support. We also observed that TCC positively moderates the relationship between the leader’s trust in the team and TRC. These results deliver meaningful theoretical and practical insights into how organizations, especially public ones, can improve team creativity and thus enhance organizational performance.

KPI-oriented process monitoring based on causal-weighted partial least squares

With the increasing demand for product quality, Key performance indicator (KPI)-oriented process monitoring plays an important role in modern industrial. Partial least squares (PLS)-based methods are widely adopted for KPI-oriented process monitoring, in which the process variable space is decomposed into a principal component subspace that has a strong correlation with KPIs and a residual subspace unrelated to KPIs, and the two spaces are monitored separately. However, if a KPI-unrelated fault occurs in a variable that has no causal relation with any KPI, but has a spurious correlation with some KPIs because of the existence of confounders, PLS based methods may mis-regard the KPI-unrelated fault as a KPI-related fault. This occurs because as a correlation analysis-based method, PLS cannot discriminate whether a variable is a real cause of KPIs or has a spurious correlation with KPIs. Motivated by this, this paper proposes two causal-weighted PLS methods for KPI-oriented process monitoring by combining the LiNGAM-based causal discovery with PLS, which first calculate causal weights based on the modified LiNGAM algorithm and bootstrap strategy, and then employ the causal weights to reweight the weight vectors of PLS to enhance the influence of causal variables in the KPI-related principal subspace and reduce the influence of spurious correlations. Case studies based on a simulated dataset, Tennessee Eastman Process dataset and field data from finishing rolling mill process show that the proposed methods can significantly reduce the false alarm rate for KPI-unrelated faults (i.e., the probability that a KPI-unrelated fault sample is mis-regarded as a KPI-related fault sample) caused by spurious correlation without significantly compromising the fault detection rate of KPI-related faults.