Search Results (94)

Rural landscapes are changing rapidly, yet many assessments remain descriptive and weakly connected to planning instruments. This study connects rural landscape analysis with planning and management by examining post-earthquake transformations in Topaktaş (Tuşba, Van), a village redesigned and relocated after the 2011 events. Using ArcGIS 10.8 and the Analytic Hierarchy Process (AHP), we integrate DEM, slope, aspect, CORINE land cover Plus, surface-water presence/seasonality, and proximity to hazards (active and surface-rupture faults) and infrastructure (Karasu Stream, highways, village roads). A risk overlay is treated as a hard constraint. We produce suitability maps for settlement, agriculture, recreation, and industry; derive a composite optimum land-use surface; and translate outputs into decision rules (e.g., a 0–100 m fault no-build setback, riparian buffers, and slope thresholds) with an outline for implementation and monitoring. Key findings show legacy footprints at lower elevations, while new footprints cluster near the upper elevation band (DEM range 1642–1735 m). Most of the area exhibits 0–3% slopes, supporting low-impact access where hazards are manageable; however, several newly designated settlement tracts conflict with risk and water-service conditions. Although limited to a single case and available data resolutions, the workflow is transferable: it moves beyond mapping to actionable planning instruments—zoning overlays, buffers, thresholds, and phased management—supporting sustainable, culturally informed post-earthquake reconstruction. Full article

Molecular networking (MN) is a novel mass spectrometry data analysis method that has advanced significantly in recent years and has rapidly emerged as a popular technique. By visualizing the connections between structurally similar compounds in mass spectra, MN greatly enhances the efficiency with which harmful substances and bioactive ingredients in cosmetics are screened. In this review, we summarize the principles and main categories of MN technology and systematically synthesize its progress in cosmetic testing applications based on 83 recent studies (2020 to 2025). These applications include screening banned additives, analyzing complex matrix components, and identifying efficacy-related ingredients. We highlight MN’s successful application in detecting prohibited substances, such as synthetic dyes and adulterants, with limits of detection (LOD) as low as 0.1–1 ng/g, even in complex matrices, such as emulsions and colored products. MN-guided isolation has enabled the structural elucidation of over 40 known and novel compounds in the analysis of natural ingredients. We also discuss current challenges, such as limitations in instrument sensitivity, matrix effects, and the lack of cosmetic-specific component databases. Additionally, we outline future prospects for expanding MN’s application scope in cosmetic testing and developing it toward computer-aided intelligence. This review aims to provide valuable references for promoting innovation in cosmetic testing methods and strengthening quality control in the industry. Full article

Healthcare is a complex sociotechnical system consisting of several groups of people interacting with each other to provide patient care. Employee commitment, empowerment, and continuous learning are crucial factors in this system. This study aims to investigate the relationship between dialogue and inquiry, a significant component of individual learning, and employee commitment in the healthcare industry. Based on organizational learning theory (OLT) and organizational commitment theory (OCT), a conceptual model was developed, and hypotheses were tested by collecting data from 346 employees working in a multi-specialty hospital in southern India. After checking the psychometric properties of the survey instrument, structural equation modeling was used to analyze data. The results indicate that (i) dialogue and inquiry positively predicts empowerment and employee commitment, (ii) empowerment is a precursor to employee commitment, and (iii) empowerment mediates the relationship between dialogue and inquiry and employee commitment. The results also support the moderating effect of system connection in the relationship between dialogue inquiry and empowerment. Further, strategic leadership interacts with empowerment to positively influence employee commitment. The findings provide valuable insights to the administrators and decision-makers in the healthcare industry for enhancing employee commitment necessary to provide low-cost and high-quality patient care. The conceptual model is first of its kind with regard to healthcare industry in India and hence makes a pivotal contribution to the advancement of literature on healthcare. Full article

The North American Thermal Analysis Society (NATAS) is pleased to announce its 51st Annual Conference, held jointly with the IX International Baekeland Symposium. This premier event unites scientists, practitioners, and students from academia, industry, and government to explore the forefront of materials science. The NATAS conference provides a dynamic forum for attendees to delve into the latest advancements in thermal analysis, rheology, and materials characterization. The technical program will highlight new developments in instrumentation and software, alongside practical applications across a wide range of industries. Concurrently, the Baekeland Symposium will showcase cutting-edge scientific, technical, and industrial innovations in the field of high-performance thermosetting polymers. The synergy of this joint meeting creates a unique platform for cross-disciplinary collaboration, fostering the exchange of novel ideas and sparking new research opportunities. Featuring technical presentations, poster sessions, and plenary lectures from renowned experts and emerging graduate students, the conference offers an ideal environment for networking and professional development. We invite you to join us to discover state-of-the-art techniques, discuss groundbreaking research, and connect with peers and leaders in the thermal and materials community. Full article

The rapid development of digital technology is reshaping the global economic landscape. However, its impact on firms’ capacity utilization rate (CUR), particularly through technological innovation, remains unclear. This study investigates this issue by developing an endogenous growth model that connects digital technology to CUR. The empirical analysis is based on data from Chinese A-share manufacturing firms. The methods employed include quantile regression, instrumental variable techniques, and various tests to explore underlying mechanisms. CUR is calculated using a special model that looks at random variations, and digital transformation is assessed using text analysis powered by machine learning. The findings indicate that digital transformation significantly enhances CUR, especially for firms with average capacity utilization levels, but has a limited effect on low- and high-end firms. Moreover, technological innovation mediates this relationship; however, factors like “double arbitrage” (involving policy and capital markets) and “herd effects” tend to prioritize quantity over quality, which constrains innovation potential. Improvements in CUR lead to enhanced firm performance and productivity, generating industry spillovers and demonstrating the broader economic externalities of digitalization. This study uniquely applies endogenous growth theory to examine the role of digital transformation in optimizing CUR. It introduces the “quantity-quality” technology innovation paradox as a crucial mechanism and highlights industry spillovers to address overcapacity while offering insights for fostering sustainable economic and social development in emerging markets. Full article

Near-Infrared Spectroscopy (NIRS) analysis technology faces numerous challenges in industrial applications. Firstly, the generalization capability of models is significantly affected by instrumental heterogeneity, environmental interference, and sample diversity. Traditional modeling methods exhibit certain limitations in handling these factors, making it difficult to achieve effective adaptation across different scenarios. Specifically, data distribution shifts and mismatches in multi-scale features hinder the transferability of models across different crop varieties or instruments from different manufacturers. As a result, the large amount of previously accumulated NIRS and reference data cannot be effectively utilized in modeling for new instruments or new varieties, thereby limiting improvements in modeling efficiency and prediction accuracy. To address these limitations, this study proposes a novel transfer learning framework integrating multi-scale network architecture with Balanced Distribution Adaptation (BDA) to enhance cross-instrument compatibility. The key contributions include: (1) RX-Inception multi-scale structure: Combines Xception’s depthwise separable convolution with ResNet’s residual connections to strengthen global–local feature coupling. (2) Squeeze-and-Excitation (SE) attention: Dynamically recalibrates spectral band weights to enhance discriminative feature representation. (3) Systematic evaluation of six transfer strategies: Comparative analysis of their impacts on model adaptation performance. Experimental results on open corn and pharmaceutical datasets demonstrate that BDSER-InceptionNet achieves state-of-the-art performance on primary instruments. Notably, the proposed Method 6 successfully enables NIRS model sharing from primary to secondary instruments, effectively mitigating spectral discrepancies and significantly improving transfer efficacy. Full article

Accurately estimating the State of Charge (SOC) of power batteries is crucial for the Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, energy management efficiency, and the safety and lifespan of the battery. However, SOC cannot be measured directly with instruments; it needs to be estimated using external parameters such as current, voltage, and internal resistance. Moreover, power batteries represent complex nonlinear time-varying systems, and various uncertainties—like battery aging, fluctuations in ambient temperature, and self-discharge effects—complicate the accuracy of these estimations. This significantly increases the complexity of the estimation process and limits industrial applications. To address these challenges, this study systematically classifies existing SOC estimation algorithms, performs comparative analyses of their computational complexity and accuracy, and identifies the inherent limitations within each category. Additionally, a comprehensive review of SOC estimation technologies utilized in BMS by automotive OEMs globally is conducted. The analysis concludes that advancing multi-fusion estimation frameworks, which offer enhanced universality, robustness, and hard real-time capabilities, represents the primary research trajectory in this field. Full article

This paper introduces a novel framework enabling natural language question answering on Piping and Instrumentation Diagrams (P&IDs), addressing a critical gap between engineering design documentation and intuitive information retrieval. Our approach transforms static P&IDs into queryable knowledge bases through a three-stage pipeline. First, we recognize entities in a P&ID image and organize their relationships to form a base entity graph. Second, this entity graph is converted into a Labeled Property Graph (LPG), enriched with semantic attributes for nodes and edges. Third, a Large Language Model (LLM)-based information retrieval system translates a user query into a graph query language (Cypher) and retrieves the answer by executing it on LPG. For our experiments, we augmented a publicly available P&ID image dataset with our novel PIDQA dataset, which comprises 64,000 question–answer pairs spanning four categories: (I) simple counting, (II) spatial counting, (III) spatial connections, and (IV) value-based questions. Our experiments (using gpt-3.5-turbo) demonstrate that grounding the LLM with dynamic few-shot sampling robustly elevates accuracy by 10.6–43.5% over schema contextualization alone, even under high lexical diversity conditions (e.g., paraphrasing, ambiguity). By reducing barriers in retrieving P&ID data, this work advances human–AI collaboration for industrial workflows in design validation and safety audits. Full article

This paper presents two innovative wireless network designs for the automation system of the Sof-Algeen water station in Zintan, addressing the challenge of connecting field instruments—such as pressure switches, solenoid valves, and differential pressure sensors—over distances of up to 4 km. Due to high costs, limited flexibility, and scalability concerns, traditional hardwired solutions are impractical for such distances. A comprehensive analysis of various Industrial Internet of Things (IIoT) network designs determined that the IEEE 802.11 standard and Phoenix Contact’s Trusted Wireless technology best meet the project’s requirements for long-distance connectivity, real-time data acquisition, system compatibility, and compliance with national telecommunications regulations. This study proposes optimal network designs using the IEEE 802.11 standard and a hybrid mesh and star network for Trusted Wireless, and evaluates these technologies based on performance, reliability, and infrastructure compatibility using simulation. The network designs were validated using the Radio Mobile tool, considering the water station’s specific terrain and wireless module parameters. The findings indicate distinct differences in structure, operation, and cost-effectiveness between the two proposed solutions, highlighting the benefits of each in achieving optimal link feasibility for robust water station automation. Full article

Pod counting of rapeseed is a critical step in breeding, cultivation, and agricultural machinery research. Currently, this process relies entirely on manual labor, which is both labor-intensive and inefficient. This study aims to develop a semi-automatic counting instrument based on transmission image processing and proposes a new algorithm for processing transmission images of pods to achieve non-destructive, accurate, and rapid determination of the seed count per pod. Initially, the U-NET network was used to segment and remove the stem and beak from the pod image; subsequently, adaptive contrast enhancement was applied to adjust the contrast of the G-channel image of the pod to an appropriate range, effectively eliminating the influence of different varieties and maturity levels on the translucency of the pod skin. After enhancing the contrast, the Sauvola algorithm was employed for threshold segmentation to remove the pod skin, followed by thinning and dilation of the binary image to extract and remove the central ridge lines, detecting the number and area of connected domains. Finally, the seed count was determined based on the ratio of each connected domain’s area to the mean area of all connected domains. A transmission imaging device that mimics the human eye’s method of counting seeds was designed, incorporating an LED transmission light source, photoelectric switch-triggered imaging slot, an industrial camera, and an integrated packaging frame. Human–machine interaction software based on PyQt5 was developed, integrating functions such as communication between upper and lower machines, image acquisition, storage, and processing. Operators simply need to place the pod in an upright position into the imaging device, where its transmission image will be automatically captured and processed. The results are displayed on a touchscreen and stored in Excel spreadsheets. The experimental results show that the instrument is accurate, user-friendly, and significantly reduces labor intensity. For various varieties of rapeseed pods, the seed counting accuracy reached 97.2% with a throughput of 372 pods/h, both of which are significantly better than manual counting and have considerable potential for practical applications. Full article

In recent years, there has been a surge in interest concerning emerging contaminants, also known as contaminants of emerging concern (CECs), due to their presence in environmental matrices. Despite lacking regulation, these chemicals pose potential health and environmental safety risks. Disodium EDTA, a widely utilized chelating agent, has raised concerns regarding its environmental impact. The present work aimed to verify the presence of Disodium EDTA at the exit of eight wastewater treatment plants discharging into some losing streams flowing within a large alluvial aquifer. Conducted in the Province of Parma (Northern Italy), the research employs a multidisciplinary approach, incorporating geological, hydrogeological, chemical, and microbial community analyses. Following a territorial analysis to assess industries in the region, through the use of ATECO codes (a classification system for economic activities), the study investigated the concentration of Disodium EDTA in effluents from eight diverse wastewater treatment plants, noting that all discharges originate from an activated sludge treatment plant, released into surface water courses feeding the alluvial aquifer. Results revealed detectable levels of Disodium EDTA in all samples, indicating its persistence post-treatment. Concentrations ranged from 80 to 980 µg/L, highlighting the need for further research on its environmental fate and potential mitigation strategies. Additionally, the microbial communities naturally occurring in shallow groundwater were analyzed from a hydrogeological perspective. The widespread presence of a bacterial community predominantly composed of aerobic bacteria further confirmed that the studied aquifer is diffusely unconfined or semi-confined and/or diffusely fed by surface water sources. Furthermore, the presence of fecal bacteria served as a marker of diffuse leakage from sewage networks, which contain pre-treated wastewater. Although concentrations of Disodium EDTA above the instrumental quantification limit have not been found in groundwater to date, this research highlights the significant vulnerability of aquifers to Disodium EDTA. It reveals the critical link between surface waters, which receive treated wastewaters impacted by Disodium EDTA, and groundwater, emphasizing how this connection can expose aquifers to potential contamination. At this stage of the research, dilution of wastewaters in surface- and groundwater, as well as hydrodynamic dispersion within the alluvial aquifer, seem to be the main factors influencing the decrease in Disodium EDTA concentration in the subsurface below the actual quantification limit. Consequently, there is a pressing need to enhance methodologies to lower the instrumental quantification limit within aqueous matrices. In a broader context, urgent measures are needed to address the risk of diffuse transport of CECs contaminants like Disodium EDTA and safeguard the integrity of surface and groundwater resources, which are essential for sustaining ecosystems and human health. Full article

The definition, characterization and implementation of Positive Energy Districts is crucial in the path towards urban decarbonization and energy transition. However, several issues still must be addressed: the need for a clear and comprehensive definition, and the settlement of a consistent design approach for Positive Energy Districts. As emerged throughout the workshop held during the fourth edition of Smart and Sustainable Planning for Cities and Regions Conference (SSPCR 2022) in Bolzano (Italy), further critical points are also linked to the planning, modeling and assessment steps, besides sustainability aspects and stakeholders’ involvement. The “World Café” methodology adopted during the workshop allowed for simple—but also effective and flexible—group discussions focused on the detection of key PED characteristics, such as morphologic, socio-economic, demographic, technological, quality-of-life and feasibility factors. Four main work groups were defined in order to allow them to share, compare and discuss around five main PED-related topics: energy efficiency, energy flexibility, e-mobility, soft mobility, and low-carbon generation. Indeed, to properly deal with PED challenges and crucial aspects, it is necessary to combine and balance these technologies with enabler factors like financing instruments, social innovation and involvement, innovative governance and far-sighted policies. This paper proposes, in a structured form, the main outcomes of the co-creation approach developed during the workshop. The importance of implementing a holistic approach was highlighted: it requires a systematic and consistent integration of economic, environmental and social aspects directly connected to an interdisciplinary cross-sectorial collaboration between researchers, policymakers, industries, municipalities, and citizens. Furthermore, it was reaffirmed that, to make informed and reasoned decisions throughout an effective PED design and planning process, social, ecological, and cultural factors (besides merely technical aspects) play a crucial role. Thanks to the valuable insights and recommendations gathered from the workshop participants, a conscious awareness of key issues in PED design and implementation emerged, and the fundamental role of stakeholders in the PED development path was confirmed. Full article

Virtual experiments (VEs) and digital twins (DTs), pivotal for realizing European strategic policies on sustainability and digitalization within Industry 4.0 and the European Green Deal, simulate physical systems and characteristics in a virtual environment, with DTs incorporating dynamic inputs from and outputs to the real-world counterpart. To ensure confidence in their use and outcomes, traceability and methods to evaluate measurement uncertainty are needed, topics that are hardly covered by the literature so far. This paper provides a harmonized definition of VEs and DTs and introduces a framework for evaluating measurement uncertainty. Furthermore, it discusses how to propagate the uncertainty of the contributions coming from the different parts of the DT. For the core part of the DT, the framework derived for VEs can be used. For the physical-to-virtual (P2V) connection and the virtual-to-physical (V2P) connection, additional sources of uncertainty need to be considered. This paper provides a metrological framework for taking all these uncertainty contributions into account while describing a framework to establish traceability for DTs. Two case studies are presented to demonstrate the proposed methodology considering industrially relevant measuring instruments and devices, namely, a coordinate measuring machine (CMM) and a collaborative robot arm (cobot). Full article

Although previous studies have extensively explored the relationship between corporate profitability and innovation, the specific impact of profitability sustainability on corporate innovation has not received sufficient attention. Furthermore, while Corporate Social Responsibility (CSR) has been recognized as significantly influencing corporate innovation, its moderating role between profitability sustainability and innovation remains underexplored. This study fills these research gaps by empirically analyzing the impact of profitability sustainability on corporate innovation and examining in detail the multiple moderating effects of CSR. This paper employs Ordinary Least Squares (OLS) and Instrumental Variables Two-Stage Least Squares (IV-2SLS) methods, using data from dairy companies listed on China’s A-share and H-share markets from 2016 to 2021, to empirically analyze the impact of profitability sustainability on corporate innovation and to examine in detail the multiple moderating effects of CSR. The results indicate that profitability sustainability significantly promotes corporate innovation. CSR directly moderates this relationship, and along with other moderating variables (financing constraints, executive compensation), it plays a complex role in this interaction, potentially inhibiting the positive connection between profitability sustainability and innovation when acting alone, but significantly enhancing innovation when interacting with CSR. Heterogeneity analysis shows that non-state-owned and H-share listed dairy companies exhibit a more significant positive effect of profitability sustainability on innovation compared to state-owned and A-share listed companies. These findings highlight the key moderating role of CSR in promoting innovation within the dairy industry and offer new perspectives on how profitability sustainability can drive corporate innovation. Full article

Health information systems (HISs) are instrumental in improving the efficiency and effectiveness of hospital operations, from managing patient data to enhancing decision-making processes. This study, which holds significant implications for the healthcare industry, aimed to identify the factors that influence users’ intentions to use HISs. The research involved interviews with healthcare professionals licensed to use the HIS of a public hospital. The survey, conducted in 2020, received 113 responses. Statistical methods of descriptive analysis, correlation, and multiple linear regression were used. Two models were examined. The first model investigated the relationship between the dependent variable of the intention to use an HIS and the independent variables of perceived time savings and perceived privacy protection. The second model explored the impact of age and education as control variables in the connection between the intention to use an HIS and time savings and privacy protection. A significant moderate positive correlation was found between the intention to use an HIS and perceived time savings, while a significant weak positive correlation was detected between the intention to use an HIS and education. Regression analysis in the first model revealed a significant connection between the intention to use an HIS and perceived time savings. However, the perceived privacy protection variable did not show a significant relationship with the dependent variable. The second model showed statistical significance in the relationship between the intention to use an HIS and education, but not with the second control variable, age. It can be concluded that education strengthens the positive impact of perceived time savings on the intention to use a health information system (HIS), a finding that has immediate and practical implications for healthcare professionals and researchers in the field of health information systems and healthcare management, underlining the importance of this research in advancing the understanding and adoption of HISs in healthcare settings. Full article