Area Size Research Articles

Improving the pore properties of porous aromatic frameworks in two aspects via partition strategy

Microporous solids are famous for their high surface area and pore size at the molecular scale, which are crucial for the applications of adsorption, separation and catalysis. An ideal porous solid would simultaneously have a high surface area and nanopores with the desired opening size. However, due to the uncontrollable reaction process of porous organic frameworks (POFs), the acquisition of such a solid is still technically limited. Herein, we reported a simple but platform-wide pore partition strategy to improve the porosity of porous aromatic frameworks (PAFs) in two aspects. This strategy was achieved by introducing a partition unit with flexible linkage to segment the original voids of PAFs into multiple micropore domains. The obtained partitioning PAFs have 130%-217% increments in surface area due to the creation of extra accessible surfaces while the pores are segmented into smaller ones. Notably, the partitioning PAFs showed significantly increased adsorption capacity for CO2 due to their improved surface area. At the same time, the narrowed pore size allowed selective capture of dye molecules by their size differences. Similar to their parent PAFs, the partitioning PAFs retained their high stability in harsh environments. A simple and universal pore partition strategy will be an important step in improving PAF porosity to desired functions.

Distinguish microphase-separated structures of diblock copolymers using local order parameters

The microphase-separated structures of block copolymers are inherently highly ordered local structures, commonly characterized by differences in domain width and curvature. By focusing on diblock copolymers, we propose local order parameters (LOPs) that accurately distinguish between adjacent microphase-separated structures on the phase diagram. We used the Molecular Assembly structure Learning package for Identifying Order parameters (MALIO) to evaluate the structure classification performance of 186 candidate LOPs. MALIO calculates the numerical values of all candidate LOPs for the input microphase-separated structures to create a dataset, and then performs supervised machine learning to select the best LOPs quickly and systematically. We evaluated the robustness of the selected LOPs in terms of classification accuracy against variations in miscibility and fraction of block. The minimum local area size required for LOPs to achieve their classification performances is closely related to the characteristic sizes of the microphase-separated structures. The proposed LOPs are potentially applicable over a large area on the phase diagram.

Scale-Dependent Habitat Nestedness and Its Implications for Anuran Conservation in the Chengdu Region: A Multi-Extent Analysis.

Nestedness in community ecology predicts that species in a species-poor site should be a subset of species of a species-rich site. A variety of ecological mechanisms have been offered to explain community nestedness; however, few studies have systematically discussed the issue of scale dependence when interpreting community nestedness. This study conducted surveys of anuran species data in the vicinity of Chengdu, Sichuan, in the summers of 2019-2020, using the transect method. The study area was divided into 23 sampling sites and 8 regions to explore the relationship between environmental factors and the nested distribution pattern of anuran communities under different sampling extents (with sampling buffers set at 1 km, 2 km, and 5 km). The WNODF (weighted-nestedness metric based on overlap and decreasing fill) results indicated that anurans exhibited a strong nested pattern at both the sampling sites scale and the regional scale. The habitat matrix test results suggested that a small-scale study area requires a correspondingly small habitat-sampling extent to effectively test for habitat nestedness. As the study area expands, the habitat-sampling range can be appropriately increased. The nested pattern of anurans in the vicinity of Chengdu can only be explained by habitat nestedness, as a Spearman's correlation analysis showed that other environmental factors (area size, connectivity index, concentration index, proximity index, and distance to the city center) were not significantly correlated with the nested sequences of sampling points and regions. Therefore, regarding the conservation strategies for anurans in the vicinity of Chengdu, we recommend prioritizing the protection of areas with higher habitat diversity.

Selective and multi-scale fusion Mamba for medical image segmentation

Given the high variability in the morphology and size of lesion areas in medical images, accurate medical image segmentation requires both precise positioning of global contours and careful processing of local boundaries. This emphasizes the importance of fusing multi-scale local and global features. However, existing CNN and Transformer-based models are often limited by high parameter counts and complex calculations, making it difficult to efficiently integrate these features. To address this challenge, we proposed two innovative optimization architectures: Selective Fusion Mamba (SF-Mamba) and Multi-Scale Fusion Mamba (MF-Mamba). SF-Mamba can flexibly and dynamically adjust the fusion strategy of local and global features according to the characteristics of the lesions, effectively handling segmentation tasks with variable morphology. MF-Mamba enhances the model’s segmentation ability for lesions of different sizes by capturing global information across scales. Based on these two structures, we constructed a lightweight SMM-UNet model, which not only significantly reduces the computational burden (with only 0.038M parameters) but also demonstrates excellent generalization ability and can efficiently adapt to various types of medical images. Extensive tests on the ISIC2017, ISIC2018, and BUSI public datasets show that SMM-UNet achieves excellent segmentation performance with an extremely low parameter cost.

Hodnocení růstu jednoho druhu a dvou hybridů Paulownia spp. v nejteplejší oblasti jižní Moravy

The size of area of plantations of fast-growing tree species, established on agricultural land in the Czech Republic (CR), is on the rise. In order to gain saw timber, it is necessary to choose other species (example: Salix spp. and Populus spp., their resulting product is wood chips), one of which could be Paulownia spp. In 2015, an experimental plot with the species of Paulownia tomentosa and two hybrids of Paulownia Shan-Tong and Paulownia Hybrid 9501 were established in South Moravia. The plants were periodically pruned up to a height of 3 m. After six years, Paulownia Hybrid 9501 had mean height of 9.4 m and DBH of 12.8 cm, P. tomentosa 7.3 m and 10.3 cm, respectively, and P. Shan-Tong 5.2 m and 9 cm, respectively. These values are well below the average, in comparison with those declared by the sellers. The limited growth was presumably caused by less precipitation and a shorter growing season, in comparison with the climate characteristics of the humid subtropical zone in which Paulownia species originated. It appears that Paulownia Hybrid 9501 could grow in conditions similar to those in South Moravia, however, with a slower growth.

A New Winter Wheat Crop Segmentation Method Based on a New Fast-UNet Model and Multi-Temporal Sentinel-2 Images

Mapping and monitoring crops are the most complex and difficult tasks for experts processing and analyzing remote sensing (RS) images. Classifying crops using RS images is the most expensive task, and it requires intensive labor, especially in the sample collection phase. Fieldwork requires periodic visits to collect data about the crop’s physiochemical characteristics and separating them using the known conventional machine learning algorithms and remote sensing images. As the problem becomes more complex because of the diversity of crop types and the increase in area size, sample collection becomes more complex and unreliable. To avoid these problems, a new segmentation model was created that does not require sample collection or high-resolution images and can successfully distinguish wheat from other crops. Moreover, UNet is a well-known Convolutional Neural Network (CNN), and the semantic method was adjusted to become more powerful, faster, and use fewer resources. The new model was named Fast-UNet and was used to improve the segmentation of wheat crops. Fast-UNet was compared to UNet and Google’s newly developed semantic segmentation model, DeepLabV3+. The new model was faster than the compared models, and it had the highest average accuracy compared to UNet and DeepLabV3+, with values of 93.45, 93.05, and 92.56 respectively. Finally, new datasets of time series NDVI images and ground truth data were created. These datasets, and the newly developed model, were made available publicly on the Web.

Biochar-based fertilizers from co-pyrolysis of algae and hazelnut shell with triple superphosphate: Physicochemical properties and slow release performance.

Phosphorus (P) is a vital nutrient for plant growth and food production. Excessive amounts of P fertilizers to a greater extent of crop P offtake are inevitably applied due to low utilization efficiency, causing environmental pollution. This study aimed to evaluate biochar-based fertilizers (BBFs) produced by co-pyrolyzing algae (A) and hazelnut shell (H) biomasses with triple superphosphate (TSP) at a ratio of 4:1 (w/w). The potential of the slow-release performance of BBFs was studied during kinetics experiments. The co-pyrolysis of biomasses with TSP yielded BBFs with significantly different properties, including electrical conductivity, pH, elemental ratios, functional groups, specific surface area and pore size characteristics. Phosphorus release from all biochars and BBFs followed the Elovich model, except for TSP and H+TSP. Kinetic studies revealed prolonged P-release times and slower release rates for BBFs compared to conventional TSP. So that, TSP released 100% of the total P, whereas H+TSP and A+TSP biochars released only 3.14% and 5.14% of the total P, respectively, during a 240-hour experiment. The slow-release performance of BBFs suggests their potential as promising alternatives to conventional phosphate fertilizers. BBFs have the potential to enhance P utilization efficiency, increase crop yield and mitigate the environmental impact of P fertilizer runoff.

Automatic Detection and Identification of Calcareous Nannofossils in Chalk Using Deep Learning: A Proof-of-Concept Study for Biostratigraphy and Climate Research

Calcareous nannofossils serve as crucial indicators for establishing the biostratigraphic age of chalk macrofossil specimens in natural science collections. Better age control of specimens collected several hundred years ago enables us to uncover the dark data hidden within these collections and incorporate these data into current research projects, examining ecosystem response to past climate change. However, the manual identification of these microscopic organisms is laborious and subjective, and so we are harnessing deep learning techniques for automatic nannofossil detection and identification. This approach required the construction of a robust dataset, currently comprising over 100,000 labelled images, complemented by the development of multiple specialised deep learning models. While some models focus on detecting target species, others are dedicated to species classification. Evaluation on an independent test set showcases the efficacy of our methodology, with the current detection model achieving a balanced accuracy of 93%. Similarly, the classification model demonstrates robust performance, attaining an average balanced accuracy of 96%. Furthermore, as well as assisting with our biostratigraphic studies, the dataset of accurately labelled images has enabled us to test other aspects of ecosystem response. For example, examining morphometric changes in nannofossils over geological time can provide valuable insights into the potential impact of current global warming on modern phytoplankton assemblages (Mancini et al. 2021). This is particularly important for coccolithophores (Young et al. 2005), which play a critical role as primary producers in the global carbon cycle. A decrease in their size could lead to bottom-up ecosystem impacts and reduced carbon sequestration (Poulton et al. 2007, Krumhardt et al. 2017). Using our dataset, we conducted a deep-learning-enhanced automatic morphometric analysis focusing on the nannofossil species, Tranolithus orionatus. Our analysis revealed that two key morphometric parameters, minor axis and area size, showed statistically significant differences between the Cenomanian stage (approximately 100.5 to 93.9 million years ago) and the post-Cenomanian stages of the Late Cretaceous (approximately 93.9 to 66.0 million years ago). Kolmogorov-Smirnov tests (Massey 1951) between the two samples yielded p-values of 0.039 for the minor axis and 0.031 for the area size. Understanding these morphometric changes is crucial due to the close parallels between current climate projections and the warming and greenhouse climate of the Late Cretaceous, particularly the Cenomanian-Turonian boundary event (Arthur et al. 1990). Insights into how organisms changed morphologically during past periods of environmental stress can help us more effectively predict future responses of organisms under similar conditions (Razmjooei et al. 2020). These findings underscore the effectiveness of our approach in automating the identification and recognition of chalk nannofossils, helping to unlock natural science collections and to address key questions related to marine response to past climate change.

Symmetry breaking and fate divergence during lateral inhibition in Drosophila.

Lateral inhibition mediates alternative cell fate decision and produces regular cell fate patterns with fate symmetry breaking (SB) relying on the amplification of small stochastic differences in Notch activity via an intercellular negative feedback loop. Here, we used quantitative live imaging of endogenous Scute (Sc), a proneural factor, and of a Notch activity reporter to study the emergence of Sensory Organ Precursor cells (SOPs) in the pupal abdomen of Drosophila. SB was observed at low Sc levels and was not preceded by a phase of intermediate Sc expression and Notch activity. Thus, mutual inhibition may only be transient in this context. In support of the intercellular feedback loop model, cell-to-cell variations in Sc levels promoted fate divergence. The size of the apical area of competing cells did not detectably bias this fate choice. Surprisingly, cells that were in direct contact at the time of SB could adopt the SOP fate, albeit at low frequency (10%). These lateral inhibition defects were corrected by cellular rearrangements, not cell fate change, highlighting the role of cell-cell intercalation in pattern refinement.

Improving the coverage area and flake size of ReS2 through machine learning in APCVD

Machine learning is playing a crucial role in optimizing material synthesis, particularly in scenarios where several parameters related to growth exhibit different and significant outcomes. An example of such complexity is the growth of atomically thin semiconductors through chemical vapor deposition (CVD), where multiple parameters can influence the thermodynamics and reaction kinetics involved in the synthesis. Herein, we performed a set of orthogonal experiments, varying the key parameters such as temperature, carries gas flux and precursor position to identify the optimal conditions for maximizing covered area and the size of rhenium disulfide (ReS2) crystals. The experimental results were used to establish correlations among the three thermodynamic variables through an artificial neural network. Contour plots were then generated to visualize the impact on the coverage and flake size of the crystals. This study demonstrates the capability of machine learning to enhance the potential of CVD-growth for the integration of 2D semiconductors like ReS2at larger scales.

New forest fire assessment model based on artificial neural network and analytic hierarchy process or fuzzy-analytic hierarchy process methodology for fire vulnerability map

Forest fires significantly disrupt global ecosystems. Many forecasting techniques predict fire activity and allocate prevention resources, but various factors are missing from the assessments, and multi-criteria decision approaches alone are insufficient. This work introduced a novel methodology combining artificial neural networks (ANN) with the analytical hierarchy process (AHP), fuzzy AHP multi-criteria methods, and spatial data to detect potential forest fire vulnerability areas using twenty variables. The results from AHP or fuzzy AHP, were processed using a multilayer perceptron with a backpropagation algorithm. The final ANN model has two objectives: first, to create a forest fire vulnerability with four classes (low, moderate, high, and very high), and second, to classify burned area sizes in regions highly or very highly vulnerable to fire. Evaluation metrics were also applied for validation. The fire model was tested using both literature review data and in situ observations. Comparative analysis showed that the burned area size model performed better than other machine learning methods, achieving an accuracy score of 89%. Meanwhile, the fire vulnerability model scored 82%. The study addresses the problem of prediction and provides an algorithm for classifying fire risk based on historical data in the Czech Republic, offering a master model for future forest management. Therefore, the ANN model, once trained, validated, and tested, does not require resetting and is effective for estimating forest fire vulnerability. Moreover, it produces results quickly, providing rapid insights into complex forest ecosystems, saving time and enhancing understanding for decision-makers.

Variable preservation of the 1991 Hudson tephra in small lakes and on land

Volcanic ash (tephra) preserved in terrestrial environments and lake sediments contains information about volcanic processes and can be used to infer eruptive parameters and frequency of past eruptions, contributing to the understanding of volcanic hazards. However, tephra deposits can undergo transformation from their initial fallout sedimentation to being preserved as a tephra layer in the sedimentary record. The process is likely to be different in lakes and in terrestrial (soil) sequences. Here we compare the thickness, mass loading and grain size of tephra layers from the 1991 eruption of Cerro Hudson, Chile, from small lakes and adjacent terrestrial settings to measurements of the tephra made shortly after the eruption. We analysed samples from 35 cores in total from six small lakes (<0.25 km2), located 76 and 109 km from the volcano in two contrasting climatic areas (cool and humid northern site, and warm and dry southern site), and made 73 measurements of tephra thickness and 11 measurements of grain size in adjacent terrestrial areas. The major element geochemistry of our samples confirmed they were from the 1991 Hudson eruption. We found that some of the measured characteristics of the preserved tephra layers were comparable to those recorded in 1991 shortly after initial deposition, but that there was considerable variability within and between locations. This variability was not predictable and lake sediments did not preserve a notably more accurate record of the fallout than terrestrial sites. However, in aggregate the characteristics of the preserved tephra was similar to those recorded at the time of deposition, suggesting that, for palaeotephra research, a sampling strategy involving a wide range of environments is more robust than one that relies on a single sedimentary record or a single type of sedimentary environment.

Deciphering the non-linear nexus between government size and inflation in MENA countries: an application of dynamic-panel threshold model

Contradictory to conventional economic theory, which foresees any increase in the size of government as inflationary, this article provides evidence that the reaction of price levels to changes in the size of government is nonlinear. The price levels do not necessarily increase in response to a rise in the size of the government but only up to a certain threshold or optimal level. Accordingly, this paper utilizes the dynamic panel threshold model to examine the threshold effects of government size (measured as government final consumption expenditure as a proportion of GDP) on inflation using a sample of 10 selected MENA countries from 1980 to 2019. The findings of this study stand out in several ways. First, the results support the nonlinear relationship between government size and inflation in the study area. Second, the government size’s estimated threshold level is equivalent to 12.46%. Third, government size negatively impacts inflation in the regime of small governments up to the threshold level. The impact turns positive once the government size goes beyond the threshold level in a regime of large size of government. These findings have ramifications for the conduct of fiscal policy. Policymakers in the MENA region can increase the size of government till it reaches the threshold level without exerting any upward pressure on price levels.

Hasht Bihisht: A Spatial Interpretation of the Location, Size, and Layout of Humayun’s Tomb-Garden in the Nizamuddin Area of Mughal Delhi

Abstract Humayun’s tomb-garden has many levels of spatial significance that include the garden’s elegant layout, monumental size, and strategic location in the Nizamuddin area of New Delhi. Previous research has shed light on spatial aspects of the tomb’s architecture, visual power, and religio-political symbolism. This article focuses on the garden’s fourfold and ninefold layout, size, and location. It interprets the garden’s hasht bihisht layout through a combination of historical geographic and spatial analysis methods. Conversion of garden measurements to gaz-i Sikandarī (a unit of measurement introduced by Sikandar Lodi [r. 1489–1517], 77.01 cm) units enabled testing hypotheses about garden layout in 5-gaz and 6.5-gaz modules. Topographic analysis sheds light on the garden’s siting in relation to natural landforms, drainage, urbanization, and antecedent architectural sites. Geometrical analysis identifies strong spatial linkages between the tomb-garden, dargāh of the Sufi saint Nizamuddin Awliyaʾ, and grave of Nizamuddin’s poet-disciple Amir Khusraw, with additional connections to antecedent tombs, the city of Dinpanah (Purana Qila), and the Grand Trunk Road from Agra to Delhi. The paper concludes with an interpretation of parallels and connections among Amir Khusraw’s poem Hasht Bihisht, Humayun’s imaginative administrative apparatus, and the hasht bihisht tomb-garden layout.

CeO2 enhanced performance of NiFe2O4-CaO for carbon capture and in-situ hydrogenation conversion to CO

NiFe2O4 exhibits excellent redox properties and is widely applied in CaO-NiFe2O4 chemical looping processes. However, rapid sintering of Ni-Fe alloy under reaction conditions limited its industrial applications. The CeO2-NiFe-CaO were synthesized through a physical mixing method with NiFe2O4 as a precursor, and its CO2 capture capacity and in-situ hydrogenation performance were conducted on ICCC-RWGS reaction under different conditions. 20CeO2-NiFe-CaO exhibited a CO yielding of 9.35 mmol/g at 650 °C. During the carbon capture stage, CO2 was adsorbed by CaO, leading to the formation of CaCO3. In the hydrogenation conversion stage, hydroxyl groups were generated on the surface, combining with CaCO3 to produce formate species, further generating H2O and CO. 15CNF-CaO exhibited superior cyclic ICCC-RWGS reaction performance compared to 15NF-CaO, with a CO yield of 1.88 mmol/g after 40 cycles. CeO2 inhibited the sintering of catalytic components, preserving a higher specific surface area and smaller particle size. Furthermore, CeO2 in 15CNF-CaO inhibited carbon deposition and enhanced in-situ hydrogenation performance, exhibiting better CaO regeneration capability and better cyclic stability. These results could provide insight for designing the DFMs for the ICCC-RWGS process.

A New Powerful Magnetic Dark Catalyst Based on rGO/Fe3O4/CdSe Nanocomposite for Ultrafast Degradation of Methylene Blue Dye.

In the present study, Rgo/Fe3O4/CdSe as a dark catalyst material was synthesized by a refluxing method. The synthesized magnetic nanocomposites were studied by various analyses such as Fourier transform infrared (FTIR), energy-dispersive X-ray spectroscopy (EDS), field emission scanning electron microscopy (FESEM), X-ray diffractometer (XRD), Raman, Zeta and vibrating sample magnetometer (VSM). Characterization of structural analysis showed that the nanocomposites were successfully synthesized. The absorption spectrum was used to determine the dark catalyst activity of rGO/Fe3O4/CdSe nanocomposite. Analysis of the absorption spectrum showed that the prepared nanocomposites degrade the MB organic dye completely (100%) after 2min of stirring in the dark, also experimenting with different pH showed that the best performance for the degradation of MB occurs in neutral and alkaline media. The Raman spectrum analysis showed that the Fe3O4/CdSe quantum dots (QDs) were correctly incorporated on the reduced graphene oxide (rGO) nanosheets. Zeta potential analysis showed that rGO/Fe3O4/CdSe has a large amount of negative charge on its surface and the surface charge increased by about 16 mV compared to the Fe3O4/CdSe compound. BET and BJH techniques were used to determine the effective surface area and pore size diameter, BET results to determine the effective surface area showed that by adding graphene to the compound, the specific surface area increased from 42.877 m2g-1 to 54.1896 m2g-1. The radical scavenger experiment showed that electrons play an essential role in the degradation process. VSM analysis showed that the prepared nanocomposites have excellent superparamagnetic behavior, this advantage enables the easy collection of nanocatalysts by magnets from wastewater after dye degradation.

Management for lymphatic malformations of head and neck.

To explore the management of lymphatic malformation in head and neck. This is a retrospective study at a single center. Data on demographic, surgery, sclerotherapy and follow-up information were collected from our Vascular Anomalies Center database. Patients with lymphatic malformation of head and neck who had undergone surgery and sclerotherapy between March 2020 and March 2024 were included. There were 94 patients in this study, the lesion sites included head (n = 60), tongue (n = 7), neck (n = 41), pharynx (n = 7), and head and neck (n = 7). Symptoms included bleeding (n = 6), infection (n = 2), dyspnea (n = 2), dysphonia (n = 4), and dysphagia (n = 4). Lymphatic malformation included macrocystic (n = 61), microcystic (n = 12) and mixed (n = 21). Surgeries for LM included radical resection, subtotal or partial resection and staged surgeries. Sclerotherapies included bleomycin monotherapy and combined sclerotherapy with ethanol and bleomycin, under ultrasound or fluoroscopy guidance. The follow-up period was from 3 months to 1 year. The therapeutic effect was evaluated according to the size of the treatment area. 55 patients, 21 patients, 11 patients and 7 patients were evaluated with excellent, good, moderate and no response, respectively. Surgical resection, sclerotherapy and the combination of the two are efficacious treatment modalities for head and neck LM. Combined with oral drugs and other new therapies may be warranted in future for challenging conditions.

Enhanced drug release through nitrendipine/hydroxypropyl methylcellulose solid dispersion via supercritical antisolvent technique

The poor water solubility of nitrendipine (NT) results in low oral bioavailability, which hinders its practical application. Hydroxypropyl methylcellulose (HPMC) is a prominent drug carrier that has been applied in the biomedical field due to its significant characteristics, such as large surface area, biocompatibility and biodegradability. In this study, an efficient drug delivery system based on the preparation of NT/HPMC solid dispersion using supercritical antisolvent (SAS) technology was proposed. The effect of different operating parameters such as solvent, host guest ratio, concentration, temperature, and pressure on NT/HPMC was optimized to obtain dispersed particles with maximum solubility. The formed solid dispersion presents non-static spherical particles with a high surface area and small particle size. Importantly, in vitro drug release studies have demonstrated that the dissolution and solubility of NT in solid dispersion are significantly enhanced compared to pure drug. In vitro bioactivity experiments showed that the NT/HPMC solid dispersion has good biocompatibility and antibacterial performance. Thus, this study indicates that solid dispersion prepared using SAS technology are considered a promising drug delivery system.

Enhancing chemical heat storage performance of nanocarbon porous particle based MgO/Mg(OH)2 composite by calcination method

The application of MgO/Mg(OH)2 in heat storage technology has been limited due to slow hydration rate and easy agglomeration. In this paper, nanoporous carbon particle (NCP) as a carrier was used to prepare NCP/MgO chemical heat storage composite materials by impregnation and calcination methods, respectively. The microstructure characterization, hydration reaction, and simultaneous thermogravimetric analysis experiments were carried out on the prepared composite materials. The results showed that the internal distribution of MgO in the composite material prepared by calcination method was more uniform, and the particle size of MgO was smaller. Under the conditions of 110 ℃ hydration temperature and 57.8 kPa water vapor partial pressure, the hydration conversion ratio of the composite material prepared by calcination method after 120 min of hydration conversion ratio reached 93 %, which was 17.7 % higher than that of the impregnated composite material and 50 % higher than that of pure MgO. Under the comprehensive influence of specific surface area, pore structure, and active component particle size, compared with pure materials and impregnated composite materials, the dehydration rate of the composite material prepared by calcination method at 300 ℃ was twice that of impregnated method and 6 times that of pure MgO, and the heat storage density reached 1039kJ/kg. After 10 cycles, the heat storage density still maintained an initial state of 95.8 %. Compared with pure materials and impregnated composite materials, NCP/MgO composite materials prepared by calcination method had better heat storage performance and higher cyclic stability.

Comparison of Mediolateral (V-shaped) versus Anteroposterior Dominant Rotator Cuff Tears: The Anteroposterior Tear Width Contributes More to Postoperative Retears than Mediolateral Length when the Tear Size Area is Similar

Tear size is a significant prognostic factor following rotator cuff repair. However, no study has investigated which dimension of the tear, the mediolateral or anteroposterior, more significantly influences the outcome when the product of the two dimensions, the tear size area, is similar. A retrospective cohort study was conducted with patients who underwent arthroscopic FTRCT repair. Two contrasting groups were derived from preoperative tear dimensions. The mediolateral dominant (MLD) group consisted of 45 FTRCTs with the mediolateral tear dimension at least 1.5 times larger than the anteroposterior, and retraction exceeding the humeral head apex. The anteroposterior dominant (APD) group included 35 FTRCTs with an inverse proportion of the dimensions and retraction short of the humeral head apex. Demographic data, pre- and postoperative magnetic resonance imaging (MRI), clinical scores, and strength were compared between the groups. The mean follow-up was 26.7 and 32.2 months in the MLD and APD groups, respectively. The tear size in area (MLD vs. APD, 521.0 vs. 523.4 mm2, P=.960) and the discrepancy between ML and AP dimensions (2.0 vs. 1.9, P=.597) were similar. However, the MLD group demonstrated significant female predominance (P=.003), dominant arm involvement (P=.007), a higher incidence of pathologic subacromial spurs (P=.016), narrower acromiohumeral distance (P<.001), shorter residual tendon (P<.001), and advanced supraspinatus muscle atrophy (P=.005). Other baseline parameters were comparable between the groups. At the one-year postoperative MRI, the MLD group demonstrated a significantly lower retear rate (4.4% vs. 31.4%, P=.001). Nevertheless, clinical scores and strength at the last follow-up did not significantly differ. In a similar tear size area, the greater AP width contributes more than the ML length in causing a retear. Female predominance, dominant arm involvement, subacromial spurs, shorter residual tendon, and supraspinatus muscle atrophy were more demonstrated in MLD tears. Surgeons should be aware that healing may be poor in APD tears despite less retraction.