Search Results (611)

Urban parks play an essential role in mitigating the urban heat island (UHI) effect driven by urbanization. A rigorous understanding of the cooling effects of urban parks can support urban planning efforts aimed at mitigating the UHI effect and enhancing urban sustainability. However, previous research has primarily focused on the maximum cooling impact, often overlooking the accumulative effects arising from spatial continuity. The present study fills this gap by investigating 74 urban parks located in the central area of Jinan and constructing a comprehensive cooling evaluation framework through two dimensions: maximum impact (Park Cooling Area, PCA; Park Cooling Efficiency, PCE) and cumulative impact (Park Cooling Intensity, PCI; Park Cooling Gradient, PCG). We further systematically examined the influence of park attributes and the surrounding urban structures on these metrics. The findings indicate that urban parks, as a whole, significantly contribute to lowering the ambient temperatures in their vicinity: 62.3% are located in surface temperature cold spots, reducing ambient temperatures by up to 7.77 °C. However, cooling intensity, range, and efficiency vary significantly across parks, with an average PCI of 0.0280, PCG of 0.99 °C, PCA of 46.00 ha, and PCE of 5.34. For maximum impact, PCA is jointly determined by park area, boundary length, and shape complexity, while smaller parks generally exhibit higher PCE—reflecting diminished cooling efficiency at excessive scales. For cumulative impact, building density and spatial enclosure degree surrounding parks critically regulate PCI and PCG by influencing cool-air aggregation and diffusion. Based on these findings, this study classified urban parks according to their cooling characteristics, clarified the functional differences among different park types, and proposed targeted recommendations. Full article

This article provides a comprehensive overview of recent studies concerning laser heat treatment (LHT) of structural and tool steels, with particular attention to the 21NiCrMo2 steel used for carburized gear wheels. Analysis includes the influence of critical laser processing conditions—including power output, motion speed, spot size, and focusing distance—on surface microhardness, hardening depth, and microstructure development. The findings indicate that the energy density is the dominant factor that affects the outcomes of LHT. Optimal results, in the form of a high surface microhardness and a sufficient depth of hardening, were achieved within the energy density range of 80–130 J/mm², allowing for martensitic transformation while avoiding defects such as melting or cracking. At densities below 50 J/mm², incomplete hardening occurred with minimal microhardness improvement. On the contrary, densities exceeding 150–180 J/mm² caused surface overheating and degradation. For carburized 21NiCrMo2 steel, the most effective parameters included 450–1050 W laser power, 1.7–2.5 mm/s scanning speed, and 2.0–2.3 mm beam diameter. The review confirms that process control through energy-based parameters allows for reliable prediction and optimization of LHT for industrial applications, particularly in components exposed to cyclic loads. Full article

The focal “hot spot” neuropathologies in COVID-19 infection are revealing footprints of a hidden underlying collapse of a novel ultrafast ultradian Piezo2 signaling system within the nervous system. Paradoxically, the same initiating pathophysiology may underpin the systemic findings in COVID-19 infection, namely the multiorgan SARS-CoV-2 infection-induced vascular pathologies and brain–body-wide systemic pro-inflammatory signaling, depending on the concentration and exposure to infecting SARS-CoV-2 viruses. This common initiating microdamage is suggested to be the primary damage or the acquired channelopathy of the Piezo2 ion channel, leading to a principal gateway to pathophysiology. This Piezo2 channelopathy-induced neural switch could not only explain the initiation of disrupted cell–cell interactions, metabolic failure, microglial dysfunction, mitochondrial injury, glutamatergic synapse loss, inflammation and neurological states with the central involvement of the hippocampus and the medulla, but also the initiating pathophysiology without SARS-CoV-2 viral intracellular entry into neurons as well. Therefore, the impairment of the proposed Piezo2-induced quantum mechanical free-energy-stimulated ultrafast proton-coupled tunneling seems to be the principal and critical underlying COVID-19 infection-induced primary damage along the brain axes, depending on the loci of SARS-CoV-2 viral infection and intracellular entry. Moreover, this initiating Piezo2 channelopathy may also explain resultant autonomic dysregulation involving the medulla, hippocampus and heart rate regulation, not to mention sleep disturbance with altered rapid eye movement sleep and cognitive deficit in the short term, and even as a consequence of long COVID. The current opinion piece aims to promote future angles of science and research in order to further elucidate the not entirely known initiating pathophysiology of SARS-CoV-2 infection. Full article

Parkinson’s disease (PD), a progressive neurodegenerative disorder, imposes growing clinical and socioeconomic burdens worldwide. Despite landmark discoveries in dopamine biology and α-synuclein pathology, translating mechanistic insights into effective, personalized interventions remains elusive. Recent advances in molecular profiling, neuroimaging, and computational modeling have broadened the understanding of PD as a multifactorial systems disorder rather than a purely dopaminergic condition. However, critical gaps persist in diagnostic precision, biomarker standardization, and the translation of bench side findings into clinically meaningful therapies. This review critically examines the current landscape of PD research, identifying conceptual blind spots and methodological shortfalls across pathophysiology, clinical evaluation, trial design, and translational readiness. By synthesizing evidence from molecular neuroscience, data science, and global health, the review proposes strategic directions to recalibrate the research agenda toward precision neurology. Here I highlight the urgent need for interdisciplinary, globally inclusive, and biomarker-driven frameworks to overcome the fragmented progression of PD research. Grounded in the Accelerating Medicines Partnership-Parkinson’s Disease (AMP-PD) and the Parkinson’s Progression Markers Initiative (PPMI), this review maps shared biomarkers, open data, and patient-driven tools to faster personalized treatment. In doing so, it offers actionable insights for researchers, clinicians, and policymakers working at the intersection of biology, technology, and healthcare delivery. As the field pivots from symptomatic relief to disease modification, the road forward must be cohesive, collaborative, and rigorously translational, ensuring that laboratory discoveries systematically progress to clinical application. Full article

Real-time quality monitoring using molten pool images is a critical focus in researching high-quality, intelligent automated welding. To address interference problems in molten pool images under complex welding scenarios (e.g., reflected laser spots from spatter misclassified as porosity defects) and the limited interpretability of deep learning models, this paper proposes a multi-granularity spatiotemporal representation learning algorithm based on the hybrid enhancement of handcrafted and deep learning features. A MobileNetV2 backbone network integrated with a Temporal Shift Module (TSM) is designed to progressively capture the short-term dynamic features of the molten pool and integrate temporal information across both low-level and high-level features. A multi-granularity attention-based feature aggregation module is developed to select key interference-free frames using cross-frame attention, generate multi-granularity features via grouped pooling, and apply the Convolutional Block Attention Module (CBAM) at each granularity level. Finally, these multi-granularity spatiotemporal features are adaptively fused. Meanwhile, an independent branch utilizes the Histogram of Oriented Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) features to extract long-term spatial structural information from historical edge images, enhancing the model’s interpretability. The proposed method achieves an accuracy of 99.187% on a self-constructed dataset. Additionally, it attains a real-time inference speed of 20.983 ms per sample on a hardware platform equipped with an Intel i9-12900H CPU and an RTX 3060 GPU, thus effectively balancing accuracy, speed, and interpretability. Full article

Tree diseases affecting Camellia japonica have emerged as a significant threat to the health and longevity of this ornamental tree, particularly in countries where this tree species is widely distributed and cultivated. Among these, Pestalotiopsis spp. have been frequently reported and are considered one of the most impactful fungal pathogens, causing leaf blight or leaf spot, in multiple countries. Understanding the etiology and distribution of these diseases is essential for effective management and conservation of C. japonica populations. The traditional methods based on pathogen isolation and pure culture cultivation for diagnosis of tree diseases are labor intensive and time-consuming. In addition, the frequent coexistence of the major pathogens with other endophytes within a single C. japonica tree, coupled with inconsistent symptom expression and the occurrence of pathogens in asymptomatic hosts, further complicates disease diagnosis. These challenges highlight the urgent need to develop more rapid, accurate, and efficient diagnostic or monitoring tools to improve disease monitoring and management on trees, including C. japonica. To address these challenges, we applied a metagenomic approach to screen fungal communities within C. japonica trees. This method enabled comprehensive detection and characterization of fungal taxa present in symptomatic and asymptomatic tissues. By analyzing the correlation between fungal dominance and symptom expression, we identified key pathogenic taxa associated with disease manifestation. To validate the metagenomic approach, we employed a combined strategy integrating metagenomic screening and traditional fungal isolation to monitor foliar diseases in C. japonica. The correlation between dominant taxa and symptom expression was confirmed. Simultaneously, traditional isolation enabled the identification of a novel species, Pestalotiopsis, as the causal agent of leaf spot disease on C. japonica. In addition to confirming previously known pathogens, our study led to the discovery and preliminary characterization of a novel fungal taxon with pathogenic potential. Our findings provide critical insights into the fungal community of C. japonica and lay the groundwork for developing improved, rapid diagnostic tools for effective disease monitoring and management of tree diseases. Full article

The olive oil sector constitutes a fundamental pillar in the Mediterranean region from socio-economic and cultural perspectives. Nonetheless, it produces significant amounts of waste, leading to numerous environmental issues. These waste streams contain valuable compounds that can be recovered and utilized as inputs for various applications. This study introduces a novel value chain for olive wastes, focused on extracting lignin from olive pomace by ionic liquids and polyphenols from olive mill wastewater, which are then incorporated as hybrid nanoparticles in the formulation of an innovative starch-based biofertilizer. This biofertilizer, obtained by using residual wastewater as a source of soluble nitrogen, acting at the same time as a plasticizer for the biopolymer, was demonstrated to surpass traditional NPK biofertilizers’ efficiency, allowing for root growth and foliage in drought conditions. In order to recognize the environmental impact due to its production and align it with the technical output, the circularity and environmental performance of the proposed system were innovatively evaluated through a combination of Life Cycle Assessment (LCA) and the Material Circularity Indicator (MCI). LCA results indicated that the initial upcycling process was potentially characterized by significant hot spots, primarily related to energy consumption (>0.70 kWh/kg of water) during the early processing stages. As a result, the LCA score of this preliminary version of the biofertilizer may be higher than that of conventional commercial products, due to reliance on thermal processes for water removal and the substantial contribution (56%) of lignin/polyphenol precursors to the total LCA score. Replacing energy-intensive thermal treatments with more efficient alternatives represents a critical area for improvement. The MCI value of 0.84 indicates limited potential for further enhancement. Full article

Mangrove forests are widely recognized for their effectiveness as carbon sinks and serve as critical ecosystems for mitigating the effects of climate change. Current research lacks comprehensive, large-scale carbon storage datasets for wetland ecosystems, particularly across Mexico and other understudied regions worldwide. Therefore, the objective of this study was to develop a high spatial resolution map of carbon stocks, encompassing both aboveground and belowground components, within the Marismas Nacionales system, which is the largest mangrove complex in northeastern Pacific Mexico. Our approach integrates primary field data collected during 2023–2024 and incorporates some historical plot measurements (2011–present) to enhance spatial coverage. These were combined with contemporary remote sensing data, including Sentinel-1, Sentinel-2, and LiDAR, analyzed using Random Forest algorithms. Our spatial models achieved strong predictive accuracy (R² = 0.94–0.95), effectively resolving fine-scale variations driven by canopy structure, hydrologic regime, and spectral heterogeneity. The application of Local Indicators of Spatial Association (LISA) revealed the presence of carbon “hotspots,” which encompass 33% of the total area but contribute to 46% of the overall carbon stocks, amounting to 21.5 Tg C. Notably, elevated concentrations of carbon stocks are observed in the central regions, including the Agua Brava Lagoon and at the southern portion of the study area, where pristine mangrove stands thrive. Also, our analysis reveals that 74.6% of these carbon hotspots fall within existing protected areas, demonstrating relatively effective—though incomplete—conservation coverage across the Marismas Nacionales wetlands. We further identified important cold spots and ecotones that represent priority areas for rehabilitation and adaptive management. These findings establish a transferable framework for enhancing national carbon accounting while advancing nature-based solutions that support both climate mitigation and adaptation goals. Full article

Background/Objectives: Antimicrobial consumption (AMC) surveillance and antimicrobial stewardship (AMS) constitute effective strategies to combat the increasing antimicrobial resistance rates worldwide. Post-Soviet countries (Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, the Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan) implemented various elements of AMC surveillance and AMS to different extents. The limited quantity and quality of data from post-Soviet countries make it difficult to assess health system performance; therefore, this region is a blind spot in global AMR monitoring. This systematic review assesses and characterises AMC surveillance and AMS implementation in post-Soviet countries. Methods: Evidence was compiled via a search in PubMed, Google Scholar, Embase, CyberLeninka, and Scopus. The eligibility criteria included AMC surveillance- and AMS-related papers in human health within defined regions and timelines. Some literature from the official websites of international and national health organisations was included in the search. Results: As a result of the searches, screening, and critical appraisal, three peer-reviewed publications and 31 documents were selected for analysis. Eleven out of fifteen countries with updated national action plans for combating antimicrobial resistance have defined AMC surveillance and AMS as strategic objectives. All 15 examined countries submitted antimicrobial consumption data to international networks and reported the existence of approved laws and regulations on antibiotic sales. However, disparities exist in the complexity of monitoring systems and AMS implementation between high-income and low-income countries in the region. Conclusions: This review provides key insights into the existing AMC surveillance and AMS implementation in former Soviet countries. Although the approach of this review lacks quantitative comparability, it provides a comprehensive qualitative framework for national-level AMC surveillance and AMS system assessment. Full article

The transition to hybrid work has become a defining feature of the post-pandemic IT sector, yet organizations lack empirical benchmarks for balancing flexibility with performance and well-being. This study addresses this gap by identifying an optimal hybrid work structure and exposing systematic perception gaps between employees and managers. Grounded in Self-Determination Theory and the Job Demands–Resources model, our research analyses survey data from 1003 employees and 252 managers across 46 countries. The findings identify a hybrid “sweet spot” of 6–10 office days per month. Employees in this window report significantly higher perceived efficiency (Odds Ratio (OR) ≈ 2.12) and marginally lower office-related stress. Critically, the study uncovers a significant perception gap: contrary to the initial hypothesis, managers are nearly twice as likely as employees to rate hybrid work as most efficient (OR ≈ 1.95) and consistently evaluate remote-work resources more favourably (OR ≈ 2.64). This “supervisor-optimism bias” suggests a disconnect between policy design and frontline experience. The study concludes that while a light-to-moderate hybrid model offers clear benefits, organizations must actively address this perceptual divide and remedy resource shortages to realize the potential of hybrid work fully. This research provides data-driven guidelines for creating sustainable, high-performance work environments in the IT sector. Full article

Background/Objectives: Epilepsy affects approximately 50 million people worldwide, with antiepileptic drugs (AEDs) remaining the cornerstone of treatment. Due to their narrow therapeutic windows, AEDs are ideal candidates for therapeutic drug monitoring (TDM). Oral fluid is increasingly considered a viable alternative to blood and urine, as it reflects the free (active) concentration of many AEDs. Its non-invasive collection, which does not require trained personnel, makes it particularly suitable for TDM in paediatric and geriatric populations. However, as samples are often stored for extended periods before analysis, analyte stability becomes a critical concern. This study aimed to evaluate the stability of four commonly used AEDs in dried saliva spot (DSS) samples. Methods: Phenobarbital, phenytoin, carbamazepine, and carbamazepine-10,11-epoxide were analysed in oral fluid samples collected via spitting and stored as DSSs. Quantification was performed using high-performance liquid chromatography with diode array detection (HPLC-DAD). Design of experiments tools were used to assess the effects of preservatives, storage temperatures, light exposure, and storage durations on analyte stability. Results: Optimal conditions were refrigeration in the dark, with a low concentration of ascorbic acid as preservative. Samples at 10 µg/mL remained stable for 14 days longer than those without preservative or reported in previous studies. Unexpectedly, at 0.5 µg/mL, analytes in samples without preservative showed greater stability. Conclusions: To our knowledge, this is the first study combining DSS and HPLC-DAD to assess the stability of these AEDs in oral fluid, providing valuable insights for non-invasive TDM strategies and supporting the feasibility of saliva-based monitoring in clinical settings. Full article

The design of small-molecule inhibitors targeting proprotein convertase subtilisin/Kein type 9 (PCSK9) remains a forefront challenge in combating atherosclerosis. While various monoclonal antibodies have achieved clinical success, small-molecule inhibitors are hindered by the unique structural features of the PCSK9 binding interface. In this study, a potential small-molecule inhibitor was identified through virtual screening, followed by molecular dynamics (MD) simulations to explore the binding mechanisms between the inhibitor and the PCSK9 protein. Binding free energies were calculated using molecular mechanics/Generalized Born surface area (MM/GBSA) with the interaction entropy (IE) method, and critical hot-spot residues were identified via alanine scanning analysis. Key residues, including ARG237, ILE369, ARG194 and PHE379, were revealed to form critical interactions with inhibitor and play dominant roles during the inhibitor’s binding. In addition, the polarization effect was shown to significantly influence PCSK9–ligand binding. The identified inhibitor exhibited highly similar binding patterns with two known active compounds, providing valuable insights for the rational design and optimization of small-molecule inhibitors targeting PCSK9. This work contributes to the development of more effective treatments for hyperlipidemia and associated cardiovascular diseases. Full article

P-glycoprotein (P-gp) is a key element of cancer treatment resistance, actively extruding cytotoxic drugs from cells and diminishing their efficacy. While its role at the plasma membrane is well established, its intracellular localization, particularly on lysosomes, is increasingly recognized as a critical contributor to drug resistance. This study investigates four innovative LightSpot fluorescent compounds to detect and quantify both membrane and lysosomal P-gp in Triple-Negative Breast Cancer (TNBC) SUM1315 and DU4475 cell lines. Results highlighted lysosomal P-gp staining by the LightSpot-FL-1, LightSpot-BrX-1, and LightSpot-BdO-1 fluorescent compounds (Mander’s coefficients > 0.8 overlapping with LAMP2 immunostaining). After both cell lines were exposed to Olaparib, a significant increase in P-gp expression level and lysosomal distribution of P-gp was detected. Indeed, after 100 µM Olaparib exposure, LightSpot-FL-1 allowed us to quantify an increase in P-gp-positive lysosome number of 1293 and 334% for SUM1315 and DU4475 cells, respectively, compared to the control. Findings suggest that P-gp may relocate to lysosomes upon drug exposure, highlighting a dual resistance mechanism involving both membrane and lysosomal P-gp. This study demonstrated the potential of LightSpot fluorescent compounds to evaluate P-gp-mediated cell resistance to treatment and emphasized the need to assess global cell P-gp expression to improve cancer diagnosis. Full article

Modern manufacturing systems operate in a global and competitive environment, where sustainability has become a critical driver for performance. Performance measurement, as a method for monitoring enterprise processes, plays a central role in aligning operational efficiency with sustainable development goals. Recently, a number of different frameworks, systems, and methods have been proposed for small and medium enterprises. Key performance indicators (KPIs) are known to be powerful tools which provide accurate information regarding bottlenecks and weak spots in companies. The purpose of the current study is to develop an advanced KPI selection/prioritization model and apply it in practice. The initial set of KPIs are obtained based on a literature review. The expert’s knowledge, outlier methods, and optimization of the enterprise analysis model (EAM) are utilized for reducing the initial set of KPIs. A fuzzy analytical hierarchy process (AHP) is implemented for prioritization of the criteria. Five different MCDM (multi-criteria decision-making) algorithms are implemented for prioritization of the KPIs. The recently introduced RADAR method is extended to the fuzzy RADAR method, providing a flexible approach for handling uncertainties. An analysis and comparison of the rankings obtained by utilizing five MCDM algorithms is performed. The prioritized KPIs provide valuable input for improving KPIs with the highest impact in particular small and medium-sized enterprises (SMEs) when implementing sustainability-aligned performance metrics. Full article

The spotted longbarbel catfish, Hemibagrus guttatus, a nationally protected Class II species in China, faces increasing threats from habitat degradation. Recently, the spotted longbarbel catfish has gained attention as a promising aquaculture species, not only for its premium flesh quality but also for its potential role in conservation through sustainable captive breeding programs. Ammonia nitrogen (ammonia-N) is a ubiquitous byproduct of intensive farming and serves as the primary environmental stressor confronting aquatic species. Elucidating the ammonia-N tolerance of spotted longbarbel catfish constitutes a critical prerequisite for its successful domestication, which is the aim of this study. We demonstrate that ammonia-N stress significantly decreases the survival rate of spotted longbarbel catfish and induces tissue damage, including gill lamella proliferation, hepatocyte blurring, and renal necrosis. Transcriptomic analysis revealed that ammonia-N stress promotes the expression of genes related to endoplasmic reticulum stress, heat-shock proteins, immune response, and apoptosis, while inhibiting antioxidant-related genes and Wnt-related genes. Enzymatic assays indicate that ammonia-N stress inhibits the activities of multiple antioxidant enzymes, including SOD, CAT, GSH, GSH-Px, and T-AOC. Microbiome analysis showed that ammonia-N stress altered the intestinal microbial community by increasing harmful bacteria (e.g., Vibrio and Aeromonas) and suppressing beneficial bacteria (e.g., Cetobacterium and Lactococcus). These findings highlight the comprehensive negative impacts of ammonia-N on the health of the spotted longbarbel catfish and provide a theoretical basis for optimizing aquaculture conditions to support the sustainable protection and domestication of the spotted longbarbel catfish. Full article