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The purpose of this research is to examine the impact of supply chain integration (SCI), demand for halal products (DHP), halal marketing (HM), process quality improvement (PQI), food safety concerns (FSCs), and health consciousness (HC) on sustainable product performance (SPP) in the halal food industry in Malaysia. A survey was conducted with 212 respondents from Malaysian halal-certified companies, and the partial least squares (PLS-SEM) method was used for the data analysis. The findings indicate a positive and significant link between SCPI, HC, PQI, and SPP. In contrast, the findings show no significant link between HM, FSC, and SPP. DHP was found to have a negative, non-significant association with SPP. This paper concludes by discussing the implications of the findings and opportunities for future research.

Artificial intelligence (AI) is a core technology driving the Fourth Industrial Revolution and serves as a foundation for sustainable technological competitiveness. Despite the rapid growth of AI-related patent filings in Korea, the overall quality of these patents remains relatively low. This study examines the determinants of patent quality in university–industry (UI) collaboration and investigates how firms’ R&D capability moderates this relationship. Using 90,782 AI patents filed with the Korean Intellectual Property Office (KIPO) between 2013 and 2023, the Patent Quality Index (PQI) was constructed by integrating forward citations, patent-family size, and the number of claims through min–max normalization. Regression analyses reveal that UI collaboration per se has no significant average effect on PQI, but firms with stronger R&D capability achieve higher patent quality through collaboration. In addition, greater collaboration depth and accumulated prior experience significantly enhance PQI, while the negative effect of technological cognitive distance is mitigated by absorptive capacity. These findings demonstrate that sustainable innovation outcomes depend not merely on the quantity of collaboration but on the synergy between qualitative collaboration structures and internal R&D capabilities. By linking open innovation theory with absorptive capacity, this study provides empirical evidence for fostering sustainable innovation ecosystems in which universities and firms co-create technological value.

Background Radiotherapy techniques have advanced significantly over the past few decades. Whole‐brain radiotherapy combined with a simultaneous integrated boost (WBRT+SIB) is increasingly used to treat limited brain metastases. Purpose To retrospectively compare helical tomotherapy and coplanar volumetric modulated arc therapy (VMAT) for WBRT with a SIB‐WBRT in patients with multiple brain metastases. Additionally, it emphasizes the importance of selecting appropriate evaluation indices when comparing SIB plans. Materials and methods Fifteen patients with 2‐ 3 metastatic lesions were retrospectively analyzed in this study. Treatment planning was performed using TomoHD and eclipse planning systems for tomotherapy and VMAT, respectively. Dose–volume histograms were used to assess the doses delivered to the target volumes and organs at risk (OARs). Quantitative metrics, including the homogeneity index (HI), conformity index (CI), and plan quality index (PQI), were used for the evaluation. Results Tomotherapy yielded significantly higher D98% values for both the planning target volume (PTV) WB and PTV_met compared with VMAT (p < 0.05). It also provided lower Dmax and Dmean values for the lenses and eyes (p < 0.001 and p < 0.02, respectively). Tomotherapy was superior in terms of PTV whole‐brain CI and PTV_met HI and CI (p < 0.05). However, no significant difference was observed in the PQI values between the techniques (p > 0.05). Conclusion Both tomotherapy and VMAT achieved acceptable target volumes and OAR doses in SIB applications. Tomotherapy showed advantages in terms of dose conformity and critical organ sparing. Moreover, this study highlights the impact of selecting appropriate evaluation indices on interpreting plan quality, particularly for complex treatment approaches such as SIB.

Unmanned aerial vehicles (UAVs) have been widely used in military and civilian applications. However, the insufficient cruising range restricts the development of UAVs due to the limitation of their battery. Inductive power transfer (IPT) is an effective way to charge the battery and solve this problem. Magnetic coupler is a key component of the IPT system, which greatly affects the power transfer and efficiency of the IPT. This paper proposes a new magnetic coupler with vertical spiral coils and ferrite PQI cores for the IPT system of UAVs, which can enhance the magnetic coupling and improve the performance of the IPT system. Finite element simulations are used to investigate the magnetic field distribution and coupling capability of the proposed magnetic coupler. In addition, an experimental platform is built to prove the validity of the IPT system using the proposed magnetic coupler. The results show that the coupling coefficient can reach 0.98, and the system transfer efficiency is 89.27% with an output power of 93 W. The IPT system also has a perfect misalignment tolerance and can achieve a stable output power.

The presented paper gives a detailed observation of the PQ related problems for WE based on the EFIG or DFIG (Extra or Doubly Fed Induction Generator. Fundamental Frequency Components are the most typical reasons of disruption of power quality. On the other hand, wind loading conditions also disrupt the power quality. Numerous modes for the removing of these problems were studied to get the rid of these fundamental frequency components of the WES (Wind Energy Systems). In the presented paper, there is substantial analysis of numerous controlling techniques were presented to eliminate these PQ and different loading conditions i.e. grid conditions and wind loading conditions. Furthermore, utilizing the WECS (Wind Energy Conversion System) as an operative filter. This analysis of DFIG based WECS help research scholars to choose the adequate control procedures while utilizing the WECS as an operative filter.

Integration of renewable energy resources and conventional grids leads to an increase in power quality issues. These power quality issues require different standards to be followed for accurate measurement and monitoring of various parameters of the power system. Conventional power quality analyzers (PQAs) are programmed to a particular standard and cannot be reconfigured by the end user. Therefore, conventional PQAs cannot meet the challenges of a rapidly changing grid. In this regard, a Compact RIO-based (CRIO-based) PQA was proposed, that can be easily reprogrammed and cope with the challenges faced by conventional PQAs. The salient features of the proposed PQA are a high processing speed, interactive interface, and high-quality data-storage capacity. Moreover, unlike conventional PQAs, the proposed PQA can be monitored remotely via the internet. In this research, a hardware-in-loop (HIL) simulation is used for performing the power-quality assessment in a systematic manner. Power quality indices such as apparent power, power factor, harmonics, frequency disturbance, inrush current, voltage sag and voltage swell are considered for validating the performance of the proposed PQA against the Fluke’s PQA 43-B.

(1) Introduction: Diabetes care is complex and delivered by different care providers in different settings across the healthcare system. Better coordination through all levels of care can lead to better outcomes and fewer hospitalizations. Prevention quality indicators (PQIs) for diabetes allow us to monitor diabetes-related avoidable admissions. The aim of this research is to assess the trend of diabetes-related preventable hospitalizations and associated risk factors in a southern Italian region. (2) Methods: The study considered all hospital admissions performed from 2008 to 2018 in the Abruzzo region, Southern Italy. Data were collected from hospital discharge records. Four different indicators were evaluated as follows: short-term complications (PQI-01), long-term complications (PQI-03), uncontrolled diabetes (PQI-14) and lower-extremity amputations (PQI-16). Joinpoint models were used to evaluate the time trends of standardized rates and the average annual percent change (AAPC). (3) Results: During study period, 8660 DRPH were performed: 1298 among PQI-01, 3217 among PQI-03, 1975 among PQI-14 and 2170 among PQI-16. During the study period, PQI-01and PQI-04 showed decreasing trends. An increasing trend was showed by PQI-16. (4) Conclusions: During an 11-year period, admissions for short-term diabetes complications and for uncontrolled diabetes significantly decreased. The use of standardized tools as PQIs can help the evaluation of healthcare providers in developing preventive strategy.

The pavement quality indicator (PQI) is a non-destructive piece of equipment for detecting the compaction degree of asphalt pavement, which can avoid primary damage to the pavement compared with the traditional core-drilling method. In this paper, the PQI method was applied to evaluate the compaction quality of asphalt pavement through data collection, calibration and statistical analysis, and the probability-distribution characteristics of compaction degree were also explored, by fitting the data with probability-distribution models. Furthermore, the optimal detection-spacing was determined by comparing the statistical results of compaction degree measured at different detection-spacings. Test results showed that the calibrated PQI data was close to the actual data of the core sample, and their error rate was within 1%. The compaction degree of the test road was mostly located between 92% and 99%, and the variable coefficient was entirely below 2%, demonstrating that the pavement-compaction quality was satisfactory and uniform. The normal distribution model, lognormal distribution model and extreme-value distribution model had relatively high accuracy in fitting the compaction-degree frequency data, while the sine-wave distribution model was low in fitting accuracy. By comparing the predicted value with the actual value of compaction degree, the normal distribution model was determined as the most appropriate model for describing the frequency distribution of compaction degree. In addition, the detection spacing was selected as 50 m, considering the reliability, accuracy and efficiency. The research results provide technical support for the compaction quality-control of asphalt pavement in a non-destructive, timely, accurate and multi-point manner, ultimately contributing to the excellent service performance and service life of asphalt pavement.

Prevention quality indicators (PQIs) constitute a set of measures that can be combined with hospital inpatient data to identify the quality of care for ambulatory care sensitive conditions (ACSC). Geographical information system (GIS) web mapping and applications contribute to a better representation of PQI spatial distribution. Unlike many countries in the world, in Portugal, this type of application remains underdeveloped. The main objective of this work was to facilitate the assessment of geographical patterns and trends of health data in Portugal. Therefore, two innovative open source applications were developed. Leaflet Javascript Library, PostGIS, and GeoServer were used to create a web map application prototype. Python language was used to develop the GIS application. The geospatial assessment of geographical patterns of health data in Portugal can be obtained through a GIS application and a web map application. Both tools proposed allowed for an easy and intuitive assessment of geographical patterns and time trends of PQI values in Portugal, alongside other relevant health data, i.e., the location of health care facilities, which, in turn, showed some association between the location of facilities and quality of health care. However, in the future, more research is still required to map other relevant data, for more in-depth analyses.

Community health centers (CHCs) are expected to play a major role in expanding primary care access under the Affordable Care Act (ACA). Although benefits of such health centers among Medicaid populations have been documented, little is known about their impact on the uninsured. Additionally, the effect of health center density on preventable hospitalizations is unknown. This study calculated health center density within five miles of California hospitals using geocoding and Haversine distance formula. Using the 2010 California inpatient dataset, rates of preventable hospitalizations were classified by Prevention Quality Indicators (PQIs). After adjusting for patient and community covariates, we found that as health center density increased, the odds ratio of preventable hospitalizations decreased in both Medicaid and uninsured patients. (OR 0.79 and 0.86 for Medicaid and uninsured respectively p<.001,.044) These findings suggest health centers are contributing to lowering preventable hospitalizations and supports plans to expand such health centers.