Search Results (1,378)

The design phase of buildings represents a dynamic and complex process, constantly evolving with modifications and feedback. It involves numerous professionals from various specialties, resulting in a fragmented and iterative trial-and-error process. Analyzing waste is the first step towards increasing the efficiency of the design process for high-rise buildings using Lean methodology. Initially, the design phase was characterized, and processes were classified into productive, contributory, and non-contributory work. Typical waste in building design was identified, analyzed, and ranked based on frequency and impact to facilitate understanding and elimination. Three traditional design stages were identified: Schematic Design (SD), Design Development (DD), and Construction Documentation (CD). A total of 33 typical wastes were classified into the eight Lean categories. Key waste ranked by the Frequency-Adjusted Importance Index (FAII) for cost, schedule, and quality metrics were late-stage design changes, waiting for resources and information, rework, and late-stage clarification of requirements. Full article

In vitro culture has become a key tool to produce seed potato (Solanum tuberosum L.) tubers, a crop of great global importance. This systematic review, based on the PRISMA-ScR methodology, analyzes the main biotechnological strategies used to obtain high-quality, healthy, and pathogen-free seeds, overcoming the limitations of traditional propagation methods. A comprehensive search was conducted in Scopus, Web of Science, and ScienceDirect (June 2025), prioritizing 65 experimental studies published between 2010 and 2025 in indexed journals. Techniques such as meristem culture, micropropagation, microtuber production, the use of temporary immersion bioreactor systems (TIBs), and synthetic seed generation were examined. These methodologies offer advantages such as accelerated propagation, higher yields, reduced use of agrochemicals, germplasm conservation, and economic efficiency. TIBs stand out for improving the survival and productivity of basic seed. This review is organized around four axes: applied techniques, key procedures, economic impact and sustainability, and future perspectives. This work constitutes a useful guide for optimizing seed tuber production using plant biotechnology. Full article

Background: Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches for improving diagnosis, monitoring, and individualized treatment of hypertension. Objectives: To critically review the current landscape of AI-enabled digital tools for hypertension management, including emerging applications, implementation challenges, and future directions. Methods: A narrative review of recent PubMed-indexed studies (2019–2024) was conducted, focusing on clinical applications of AI and digital health technologies in hypertension. Emphasis was placed on real-world deployment, algorithmic explainability, digital biomarkers, and ethical/regulatory frameworks. Priority was given to high-quality randomized trials, systematic reviews, and expert consensus statements. Results: AI-supported platforms—including remote blood pressure monitoring, machine learning titration algorithms, and digital twins—have demonstrated early promise in improving hypertension control. Explainable AI (XAI) is critical for clinician trust and integration into decision-making. Equity-focused design and regulatory oversight are essential to prevent exacerbation of health disparities. Emerging implementation strategies, such as federated learning and co-design frameworks, may enhance scalability and generalizability across diverse care settings. Conclusions: AI-guided titration and digital twin approaches appear most promising for reducing therapeutic inertia, whereas cuffless blood pressure monitoring remains the least mature. Future work should prioritize pragmatic trials with equity and cost-effectiveness endpoints, supported by safeguards against bias, accountability gaps, and privacy risks. Full article

Given the multi-basin raw material base for coking that has been formed at most industry enterprises, there is an urgent need to optimize the component composition and improve the basic technological methods of coal raw material preparation, taking into account the petrographic characteristics of coal batches. A comprehensive study of the components included in a coke chemical enterprise’s coking raw material base was carried out. The work used standardized methods for studying coal and coal batches’ technological and plastic–viscous properties. The qualitative characteristics of coke were determined using physical–mechanical and thermochemical methods of studying standardized indicators: crushability (M₂₅), abrasion (M₁₀), reactivity (CRI), post-reaction strength (CSR), and specific electrical resistance (ρ). The results were analyzed using the licensed Microsoft Excel computer program. Based on the results of proximate, plastometric, and petrographic analyses of the studied coal samples and data from experimental industrial coking, proposals were made to optimize the component composition, properties of the coal batch, and technology for its preparation for coking. The established inverse dependence of Gibbs free energy (ΔG_f,total) on the reaction capacity of coke CRI and its direct reliance on its post-reaction strength CSR confirmed the feasibility of using ΔG_f,total as a thermodynamic predictive parameter for optimizing and compiling coal batches that produce less reactive, stronger coke. This made it possible to improve the quality indicators of metallurgical coke. Thus, according to the M₂₅ crushability index, the mechanical strength increased by 0.6%, and the M₁₀ abrasion decreased by 0.4%. Significant improvements in thermochemical properties and an increase in the orderliness of the carbon structure were recorded: the CRI reactivity decreased by 3.1%, the CSR post-reaction strength increased by 8.3%, and the specific resistance decreased by 8.4%. Full article

Healthcare systems worldwide face growing challenges related to staff shortages, excessive workload, and deteriorating working conditions, which compromise both staff well-being and care quality. Despite these issues, there is a lack of validated tools that capture healthcare professionals’ subjective perceptions of resource adequacy. This study presents the development and initial validation of the Staff Resource Adequacy Questionnaire for Healthcare Professionals (SRAQ-HP), a multidimensional tool designed to assess staffing adequacy and workload, quality of care, and working conditions and support. The development process followed a mixed-methods design, incorporating theoretical foundations from Kanter’s empowerment theory, role enactment models, and professional competence frameworks. The initial item pool of 32 statements was reduced to 26 through expert reviews, focus groups, and pilot testing (n = 35). Content validity index (CVI = 0.931) and face validity index (FVI = 0.976) demonstrated high content relevance and clarity. Cronbach’s alpha for the full scale was 0.841, confirming internal consistency. Expert re-review confirmed strong content (S-CVI/Ave = 0.931) and face validity (FVI = 0.976) for the final 26-item version. Three core dimensions were retained: Staffing Adequacy and Workload, Quality of Care, and Working Conditions and Support. The SRAQ-HP provides a novel, evidence-based approach to systematically assess workforce sufficiency and support structures in clinical settings. It can guide decision-making in healthcare institutions and inform national workforce policies. Further research with larger and more diverse samples is needed to confirm its factorial validity and practical applicability. Full article

Background: Health promotion programs based on physical activity have gained increasing attention due to their potential to enhance employees’ physical and mental well-being, particularly in university settings, where academic and administrative staff are vulnerable to chronic stress, sedentary lifestyles, and work-related disorders. This study evaluates the effectiveness of the “University on the Move” program, an exercise-based workplace health promotion intervention implemented at the University of L’Aquila, Italy. Methods: An intervention study was conducted on 29 university employees participating in a supervised physical exercise program. Their anthropometric parameters, biochemical markers, cardiovascular risk factors, blood pressure, and work ability index were assessed at baseline (t0), three months (t1), and six months (t2), with a one-sample pre–post-test quasi-experimental design. Results: Significant improvements were observed in the cardiovascular risk factors, particularly in the female subgroup, e.g., the total cholesterol decreased by 20.8 mg/dL (p < 0.01), the LDL cholesterol decreased by 16.1 mg/dL (p < 0.01), and the fasting glucose decreased by 7.4 mg/dL (p < 0.01). Although the body mass index values remained stable, these metabolic improvements indicate beneficial effects independent of weight changes. The work ability index remained stable. The participation rates varied, with lower adherence to the training sessions. Conclusions: The study has some limitations (small sample size, no controlled design), all related to the primary aim of the preventive project targeted at the University employees who freely engaged in the protocol. Despite the low adherence (only about 30%), the metabolic improvements still suggest that structured workplace physical activity programs can positively impact employees’ health, even without significant weight loss, thus supporting the effectiveness of workplace health promotion and primary prevention interventions for an improved quality of life. Further research should explore long-term adherence and the organisational factors influencing participation. Full article

Staining variability in histopathological images compromises automated diagnostic systems by affecting the reliability of computational pathology algorithms. Existing normalization methods prioritize color consistency but often sacrifice critical morphological details essential for accurate diagnosis. This work proposes a novel deep learning framework, integrating enhanced residual learning with multi-scale attention mechanisms for structure-preserving stain normalization. The approach decomposes the transformation process into base reconstruction and residual refinement components, incorporating attention-guided skip connections and progressive curriculum learning. The method was evaluated on the MITOS-ATYPIA-14 dataset containing 1420 paired H&E-stained breast cancer images from two scanners. The framework achieved exceptional performance with a structural similarity index (SSIM) of 0.9663 ± 0.0076, representing 4.6% improvement over the best baseline (StainGAN). Peak signal-to-noise ratio (PSNR) reached 24.50 ± 1.57 dB, surpassing all comparison methods. An edge preservation loss of 0.0465 ± 0.0088 demonstrated a 35.6% error reduction compared to the next best method. Color transfer fidelity reached 0.8680 ± 0.0542 while maintaining superior perceptual quality (FID: 32.12, IS: 2.72 ± 0.18). The attention-guided residual learning framework successfully maintains structural integrity during stain normalization, with superior performance across diverse tissue types, making it suitable for clinical deployment in multi-institutional digital pathology workflows. Full article

Small streams often underwent engineering works conducted without special care for the hydromorphological and ecosystemic consequences. To allow small Walloon watercourses to comply with the European Water Framework Directive, renaturation is required. However, the cost of such projects is often prohibitive for small streams. Therefore, the Rivialis project aims at designing a methodology to support such works, based on a “small river quality index” that requires the collection of various data under the form of an “ID card” of the investigated river reach, allowing to obtain a synthetic overview of the key features of the study reach. Such an ID card, and ultimately the index, should include the most relevant components among existing morphological and biological indicators. To reduce the project costs, the number of field measurements to build this ID card should be limited; the data should be obtained from online and publicly available data sources or easily collected on site. In this paper, key metrics are identified from the literature. They are then determined along a reach of the Petit Bocq River with the aim of assessing those that can be obtained at a low cost from available databases and those that require more costly field investigations. The results show that combining available databases and numerical simulations allows determining a river reach ID card yielding a first set of valuable information at a low cost. Field surveys can then be limited to the verification of these values and to the collection of biological information. Full article

Automotive interior lighting has progressed from basic functional illumination to sophisticated aesthetic systems emphasizing chromatic stability under thermal variations. This study enhances an RGB temperature compensation algorithm for LEDs, extending it to an RGBW solution. While several approaches for LED temperature compensation have been proposed in the literature, none have addressed a complete RGBW solution where the white channel is derived and actively adjusted on thermal variations. This research aims to fill this gap by extending an RGB algorithm to RGBW and validating it under realistic automotive conditions. While the proposed compensation strategies are general and may be applied to other LED systems, the automotive interior lighting domain has been selected as a representative case study because it combines stringent chromatic stability requirements ($\Delta u^{\prime }{v}^{\prime }\le 0.01$) and high industrial relevance. Leveraging Infineon’s LITIX™ LED drivers, experimental results show that the algorithm maintains chromatic stability with deviations below $\Delta u^{\prime }{v}^{\prime }=0.00562$ in RGB mode and $\Delta u^{\prime }{v}^{\prime }=0.0067$ in RGBW mode across the tested temperature range. The addition of the white channel improves the color rendering index (CRI) by up to 58.9 points (from 19.7 to 78.6) while preserving color quality. Compared to previous works limited to RGB systems, our approach provides the first practical RGBW compensation algorithm experimentally validated under realistic automotive conditions. Full article

This study aimed to provide basic data according to age on objective sleep duration distribution and sleep characteristics via subjectivity, and to determine the prevalence of insufficient sleep and related sleep parameters in the general Japanese population. Data from the second survey of the Japan Multi-Institutional Cohort (J-MICC) Daiko Study were used for the analysis, with 2091 participants (1556 women; 58.6 ± 9.8 years old) included. Questionnaires included subjective sleep duration, perceived sufficiency, regularity, the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Morningness–Eveningness Questionnaire (MEQ), and Epworth Sleepiness Scale (ESS). Sleep measurements were taken via actigraphy for one week. In total, 247 (11.8%) respondents reported insufficient sleep and 953 (45.6%) reported somewhat insufficient sleep. Working-age adults had shorter subjective and measured sleep durations than those aged ≥ 60 years. About 20% of those aged ≥ 50 years and more than 30% of those aged < 50 years reported ≥2 h of sleep deprivation. Perceived insufficient sleep was associated with irregular sleep but not sleep efficiency or sleep latency. Additionally, sleep duration perceived as insufficient varied among individuals. Individual differences in sleep duration and sleep efficiency were greater than those based on age. In conclusion, implementing measures to address sleep deprivation in Japan’s working-age population are essential, and future epidemiological studies should consider individual differences. Full article

Improving sowing quality is crucial for ensuring wheat emergence and healthy growth. To address issues of poor wheat sowing quality, such as uneven sowing depth and inadequate soil coverage, in the Yangtze River Delta region of China, this study systematically analyzed the effects of the implement’s structural and operational parameters on sowing quality. Based on this analysis, a double-shaft rotary tillage and double-press seeder was designed. Protrusions on the grooving press roller are used to form seed furrows, rotary tiller blades cover the seeds with soil, and the rear press roller compacts the soil. DEM-MBD (discrete element method–multibody dynamics) coupled simulations, combined with single-factor and central composite design (CCD) experiments, were conducted with seeding depth as the evaluation index and four experimental factors: the protrusion height on the press grooving roller, forward speed, seed mass in the seed box, and straw mulching amount. The optimal protrusion height was 29 mm. The effects of rotary tiller blade working depth, rotational speed, and forward speed on soil-covering mass and its coefficient of variation were evaluated through discrete element method (DEM) simulations. The optimal working depth and rotational speed were found to be 55 mm and 350 r·min⁻¹, respectively, based on single-factor and Box–Behnken Design experiments. Field experiments based on optimized parameters showed results consistent with the simulations. The qualified rate of seeding depth decreased as forward speed increased. The optimal forward speed was 4.5 km·h⁻¹, at which the average seeding depth was 25.7 mm, the qualified seeding depth rate was 90%, the soil-covering mass within a 50 cm² area was 143.2 g, and the coefficient of variation was 13.21%, meeting the requirements for wheat sowing operations. Full article

Mechanised agricultural operations are often performed individually, under minimal supervision and across a wide range of unfavourable working conditions, resulting in a complex mixture of hazards and external stressors that severely affect safety conditions. Socio-technical and environmental constraints significantly affect safety culture and require continuous performance adjustments to overcome timing pressures, resource limitations, and unstable weather conditions. This study introduces a FRAM-based safety culture model that embeds the thoroughness-efficiency trade-off (ETTO) in four distinct operational modes that adhere to specific safety cultures, namely, thoroughness, risk awareness, compliance, and efficiency. This model has been instantiated for mechanised ploughing: foreground task functions were coupled with background functions that represent socio-technical constraints and environmental variability, while severity classes for potential incidents were derived from the US OSHA accident database. The framework was also supported by a semi-quantitative Resonance Index based on severity and coupling strength, the Total Resonance Index (TRI), to assess how variability propagates in foreground functions and to identify hot-spot functions where small adjustments can escalate into high resonance and hazardous conditions. Results showed that the negative effects on functional resonance generated by safety detriment on TRI observed between compliance and effective working modes were three times larger than the drift between risk awareness and compliance, demonstrating that efficiency comes with a much higher cost than keeping safety at compliance levels. Extending the proposed approach with quantitative assessments could further support the management of socio-technical and environmental drivers in mechanised farming, strengthening the role of safety as a competitive asset for enhancing resilience and service quality. Full article

Background/Objectives: Colorectal surgeons continue to care for an aging cancer population with increasing comorbidities and frailty. Frailty, characterized by a systemic physiologic decline associated with aging, is an increasingly popular focus in surgical outcomes research. This retrospective study investigates how frailty impacts outcomes in the octogenarian and nonagenarian populations undergoing surgical treatment for colon cancer. Methods: Data from the National Surgical Quality Improvement Program (NSQIP) colectomy-targeted variables dataset from 2015 to 2021 were utilized for this analysis, including patients 80 years of age and older. Frailty was assessed using the five-factor modified frailty index (mFI-5). The study examined post-operative outcomes across frailty groups in this population. Results: From 2015–2021, there were 10,671 patients aged 80 years and older who underwent colectomy for colon cancer, of whom 1259 (11.8%) were 90 years or older and 2844 (26.7%) were severely frail. Frailty significantly impacted post-operative colectomy outcomes in this population. On univariate analysis, frail patients had higher rates of pneumonia (p = 0.015), unplanned intubation (p = 0.012), stroke (p < 0.001), myocardial infarction (p = 0.011), readmission (p < 0.001), long length of stay (p < 0.001), and mortality (p < 0.001) compared to non-frail patients. On multivariate analysis, severe frailty (mFI-5 of 2 or more) was associated with an increased odds of unplanned intubation (aOR 2.41, 95% CI 1.27–4.59), long length of stay (aOR 1.73, 95% CI 1.44–2.09), readmission (aOR 1.84, 95% CI 1.42–2.39), and mortality (aOR 1.95, 95% CI 1.20–3.15) compared to non-frail patients. Conclusions: Frailty plays a critical role in influencing the outcomes of octogenarians and nonagenarians undergoing colectomy for colon cancer within the NSQIP dataset. Future work should investigate whether addressing frailty prior to surgery in this population can improve patients’ post-operative courses. Full article

One of the crucial types of information needed to guarantee the secure operation of power systems is their voltage stability condition. This is particularly true for power systems operating at peak hours or under abnormal conditions, such as contingencies. The literature shows several methods for voltage stability assessment; however, they are either accurate and computationally burdensome or less accurate and computationally efficient. The main goal of this research work is to propose methods that are both accurate and fast, features that are especially important in strict real-time operating conditions. Two new methods for computing the maximum loadability and the voltage stability margin of power systems are proposed. Both methods use a powerful, second-order, and non-divergent power flow with an optimally computed step size; however, each of them is initialized differently. Very high-quality initializations are obtained by using a linear voltage stability index and sensitivity analysis factors. This combination leads to a fast, robust, and accurate method, suited for strict real-time power system operation. The proposed methods require 90% fewer power flow runs compared with conventional methods, such as the continuation method for small systems, and tend to require even fewer power flow runs for larger systems. Computer simulations of the proposed methods use small benchmarks to large realistic power systems, showing that the requirements for real-time use—namely accuracy, robustness, and computational efficiency—are met. Full article

This paper explores the application of transform-domain sparsification and compressed sensing (CS) techniques to improve the efficiency and quality of magnetic resonance imaging (MRI). We implement and evaluate three sparsifying methods—discrete wavelet transform (DWT), fast Fourier transform (FFT), and discrete cosine transform (DCT)—which are used to simulate subsampled reconstruction via inverse transforms. Additionally, one accurate CS reconstruction algorithm, basis pursuit (BP), using the L₁-MAGIC toolbox, is implemented as a benchmark based on convex optimization with L₁-norm minimization. Emphasis is placed on basis pursuit (BP), which satisfies the formal requirements of CS theory, including incoherent sampling and sparse recovery via nonlinear reconstruction. Each method is assessed in MATLAB R2024b using standardized DICOM images and varying sampling rates. The evaluation metrics include peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index measure (SSIM), execution time, memory usage, and compression efficiency. The results show that although discrete cosine transform (DCT) outperforms the others under simulation in terms of PSNR and SSIM, it is inconsistent with the physics of MRI acquisition. Conversely, basis pursuit (BP) offers a theoretically grounded reconstruction approach with acceptable accuracy and clinical relevance. Despite the limitations of a controlled experimental setup, this study establishes a reproducible benchmarking framework and highlights the trade-offs between the quality of transform-based reconstruction and computational complexity. Future work will extend this study by incorporating clinically validated CS algorithms with L₀ and nonconvex Lp (0 < p < 1) regularization to align with state-of-the-art MRI reconstruction practices. Full article