In the current scenario, energy generation is relied on the portable gadgets with more efficiency paving a way for new versatile and smart techniques for device fabrication. 3D printing is one of the most adaptable fabrication techniques based on designed architecture. The fabrication of 3D printed energy storage devices minimizes the manual labor enhancing the perfection of fabrication and reducing the risk of hazards. The perfection in fabrication technique enhances the performance of the device. The idea has been built upon by industry as well as academic research to print a variety of battery components such as cathode, anode, separator, etc. The main attraction of 3D printing is its cost-efficiency. There are tremendous savings in not having to manufacture battery cells separately and then assemble them into modules. This review highlights recent and important advances made in 3D printing of energy storage devices. The present review explains the common 3D printing techniques that have been used for the printing of electrode materials, separators, battery casings, etc. Also highlights the challenges present in the technique during the energy storage device fabrication in order to overcome the same to develop the process of 3D printing of the batteries to have comparable performance to, or even better performance than, conventional batteries.

: Superhydrophobic surfaces have great application prospects due to their unique surface- wetting characteristics. However, superhydrophobic surfaces' micro-nano binary rough structure and low surface energy components are easily damaged or lost by wear and grease, which affects their durability and limits their practical application, so it is of great significance to study the durability of superhydrophobic surfaces. The review first introduces the preparation methods and application fields of superhydrophobic surfaces, then sorts out the test methods for the durability performance of superhydrophobic surfaces, methods to improve the durability of superhydrophobic surfaces are summarized, and finally points out some problems in the current research on the durability of superhydrophobic surfaces, aiming to have a comprehensive understanding of the research progress of durable superhydrophobic surfaces, provide some theoretical guidance for the development of durable superhydrophobic surfaces, and look forward to the development direction and trend of durable superhydrophobic surface research in the future. : There have been substantial improvements achieved in the preparation techniques to increase the mechanical durability of superhydrophobic surfaces and widen their applications. : This review examines pertinent patents and articles on the fabrication of superhydrophobic surfaces from both domestic and foreign sources. : The paper introduces the fundamental preparation methods for superhydrophobic surfaces and examines three common techniques: The template method, the spraying method, and the etching method. Additionally, the study discusses these preparation methods and presents future development trends based on the latest research findings. Aiming at the mechanical durability of superhydrophobic surfaces, the test methods for the durability performance of superhydrophobic surfaces under mechanical action are reviewed. The paper also suggests four key methods for enhancing the durability of superhydrophobic surfaces. : The refinement of superhydrophobic surface preparation techniques plays a crucial role in enhancing durability and broadening the applications of these surfaces. Additionally, it holds the potential to unlock new possibilities across various domains. The future holds promising prospects for the invention of additional patents and papers focused on superhydrophobic surfaces.

The investigation of soil is a particularly important stage of structural design. Cone penetration tests (CPTs) are the most common soil investigation techniques. The results of these tests provide information about the values of cone resistance (qc) and sleeve friction (fs), which correspond to depth. Previous studies have shown that the ratio of sleeve friction to cone resistance depends on the particle size distribution in soil and its use for soil classification. Unfortunately, as an analysis of the literature shows, there is no such classification for coarse-grained soils. This paper presents statistically significant differences in the ratio of fs to qc in coarse-grained soils. Based on the research performed, the proposed coefficients depend on the classification of coarse-grained soils with respect to the size of the soil particles. The data investigated were obtained from study reports on 35 sites (5934 tests) at which the main type of soil was coarse-grained and contained different sizes of particles. Following a statistical analysis, five groups of tested coarse-grained soils, silty fine sand, clayey fine sand, fine sand, medium sand and gravelly coarse sand together with gravel, are derived. The analysed data show statistically significant differences in the ratio of fs to qc considering this particular type of soil. A ratio of fs to qc with a probability of 95% is proposed for sandy soils. The values for silty fine sand, clayey fine sand, fine sand, medium sand and gravelly coarse sand mixed with gravel are 0.009459, 0.010982, 0.009268, 0.008001 and 0.006741, respectively. A linear relationship between the fs and qc indexes is also suggested.

To determine the criterion validity and test-retest reliability of isometric finger-strength testing in 6 differentiated grip techniques for the assessment of bouldering ability among male climbers. We recruited participants at climbing gyms in Sweden and through online advertisements. We included climbers over 15 years of age with a minimum bouldering performance level of 17 International Rock Climbing and Research Association (IRCRA) for men and 15 IRCRA for women. We tested unilateral, maximal isometric peak finger strength in the front 3 drag, half crimp, closed crimp, 35 sloper, 45 × 90-mm, and 90 × 90-mm pinch through maximal force deloaded of a force plate. We analyzed criterion validity, test-retest reliability, and capacity to determine bouldering performance ability using a stepwise multivariable regression model. Women were excluded from the analysis due to insufficient sample size (n=16). Thirty-two male participants were included in the primary analysis. The median (interquartile range) age in the advanced and elite group was 27 (25; 35) and 23 (22; 32) years, respectively. The half crimp for the participants' weak and strong hand displayed the highest ability to determine bouldering grade performance, explaining 48% to 58% of the variance. In the stepwise regression, maximal strength in the half crimp and the front 3 drag collectively explained 66% of the variance for performance. Strength in the half crimp proved the most important performance indicator. The results of this study provide a reliable and valid framework for maximal isometric peak finger-strength testing in advanced and elite male boulderers.

Cloud computing has attracted wide attention from both academia and industry, since it can provide flexible and on-demand hardware and software resources as services. Energy consumption of cloud servers is the main concern of cloud service providers since reducing energy consumption can bring them a lower operation cost (and hence a higher profit) and alleviate carbon footprints to the environment. Typically, the common power management techniques for enhancing energy efficiency would make cloud servers more vulnerable to soft errors and hence adversely impact the quality of services. Thus, reliability cannot be ignored in the design of methodologies for improving the energy efficiency of cloud servers. In this article, we aim to minimize the energy consumption of cloud servers under the soft-error reliability constraint by configuring the size and speed of servers. Specifically, we first derive the expected reliability based energy consumption of cloud servers to formulate the reliability-constrained energy minimization problem. We then leverage the reinforcement learning technique to obtain an optimal server configuration solution that maximizes system energy efficiency while maintaining the system reliability constraint. Finally, we perform extensive simulation experiments to analyze the relationship between system energy consumption and server configuration under varying arrival rates and execution requirements of service requests. Comparative experiments are also performed to validate the efficacy of the proposed learning-based server configuration scheme. Results show that compared to a benchmark method, the energy saved by the proposed scheme can reach up to 31.5%.

In Indonesia, the number of Type 2 diabetes cases is increasing rapidly, making it the third leading cause of death and among the leading noncommunicable disease healthcare expenditures in the country. Thus, there is a critical need for Indonesians with Type 2 diabetes to perform better self-care to optimize their health and prevent the onset of comorbidities. This study was designed to investigate the influence of knowledge, depression, and perceived barriers on Type 2 diabetes self-care performance in Indonesia. A cross-sectional study was conducted on 185 patients with Type 2 diabetes, with demographic, diabetes history, obesity status, diabetes knowledge, depression, perceived barriers, and self-care performance data collected. The Indonesian version of the Revised Diabetes Knowledge Test, Depression Anxiety Stress Scale, Perceived Barrier Questionnaire and Self-Care Inventory-Revised were used. Descriptive, bivariate, and multiple linear regression analyses were performed. Study participants were found to have moderate diabetes self-care performance scores. Annual eye checks, blood glucose self-monitoring, healthy diet selection, and regular exercise were the least common self-management techniques performed and were consistent with the perceived difficulties of the participants. Being illiterate or having an elementary school education (β = 4.59, p = .002), having a junior or senior high school education (β = 3.01, p = .006), having moderate depression (β = -0.92, p = .04), diabetes knowledge (β = 0.09, p = .006), and perceived barriers (β = 0.31, p < .001) were found to explain 40% of the variance in self-care performance. Educational level, depression, and perceived barriers were the strongest factors that impacted Type 2 diabetes self-care performance in this study. Nurses should not only provide diabetes education but also identify barriers to diabetes self-care early, screen for the signs and symptoms of depression, and target patients with lower levels of education.

Effective plant virus enrichment using carbon nanotubes and microfluidics

Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in these works, include natural language processing, e.g., named entity recognition, as well as graph networks representing online social networks. However, large-scale studies that take into account Reddit’s unique structure are scarce. In this contribution, similarity between subreddits is explored. Specifically, subreddit posts (from 3189 subreddits, spanning the year 2022) are processed using NER to build graph networks which are further mined for relations between subreddits. The evaluation of obtained results follows the state-of-the-art approaches used for a similar problem, i.e., recommender system metrics, and applies recall and AUC. Overall, the use of Reddit crossposts discloses previously unknown relations between subreddits. Interestingly, the proposed approach may allow for researchers to better connect their study topics with particular subreddits and shows promise for subreddit similarity mining.

The geotechnical characterization of the subsurface is a key requirement for most soil investigations, incl. those for reclaiming landfills and waste dumps associated with mining operations. New sensor technology, combined with intelligent analysis algorithms, allow for a faster and less expensive acquisition of the necessary information without loss of data quality. The use of advanced technologies to support and back up common site investigation techniques, such as cone penetration testing (CPT), can enhance the underground characterization process. This study aims to investigate the possibilities of image analysis for material recognition to advance the geotechnical characterization process. The grey level co-occurrence matrix (GLCM) image processing technique is used in a wide range of study fields to estimate textures, patterns and structure anomalies. This method was adjusted and applied to process the video recorded during a CPT sounding, in order to distinguish soil types by its changing surface characteristics. From the results of the video processing, it is evident that the GLCM technique can identify transitions in soil types that were captured in the video recording. This enables the prospect of image analysis not just for soil investigations, but also for monitoring of the conveyor belt in the mining field, to allow for efficient preliminary decision making, material documentation and quality control by providing information in a cost effective and efficient manner.

Hydrogen embrittlement (HE) is a broadly recognized phenomenon in metallic materials. If not well understood and managed, HE may lead to catastrophic environmental failures in vessels containing hydrogen, such as pipelines and storage tanks. HE can affect the mechanical properties of materials such as ductility, toughness, and strength, mainly through the interaction between metal defects and hydrogen. Various phenomena such as hydrogen adsorption, hydrogen diffusion, and hydrogen interactions with intrinsic trapping sites like dislocations, voids, grain boundaries, and oxide/matrix interfaces are involved in this process. It is important to understand HE mechanisms to develop effective hydrogen resistant strategies. Tensile, double cantilever beam, bent beam, and fatigue tests are among the most common techniques employed to study HE. This article reviews hydrogen diffusion behavior, mechanisms, and characterization techniques.

Partial volume correction in longitudinal tau PET studies: is it really needed?

Geothermal energy, harnessed through the Earth's thermal resources, emerges as an enduring and sustainable remedy to tackle the urgent concern of water scarcity through desalination. By harnessing the innate thermal reservoirs within the planet, geothermal energy provides a distinctive avenue for generating heat and electricity, particularly when synergistically aligned with desalination procedures. To understand the insights of the implications of geothermal energy for desalination, we conducted a comprehensive literature review analysis. Main objectives of the review are to (i.) disseminate insights into geothermal-driven desalination, (ii.) explore potentialities and obstacles of geothermal-driven desalination, and (iii.) evaluate forthcoming environmental, socio-economic predicaments associated with this approach. We selected 50 peer reviewed scholarly communications published using Google Scholar from 2000 to 2022, focusing on impactful English-language publications within esteemed scientific journals. We found multi-effect evaporation/distillation (MED); multi-stage flash distillation (MSF); thermal vapor compression (TVC) and mechanical vapor compression (MVC). Membrane processes include electrodialysis (ED), reverse osmosis (RO), and membrane desalination are the common techniques used across mainly in Australia, USA, UAE, Sub-Saharan and African nations, and Japan. The integration of innovative designs of these methods can enhance the efficacy and cost-efficiency of geothermal desalination systems. Furthermore, critical environmental, social, and economic concerns linked to geothermal-driven desalination were identified. We noted that high-capacity factor for stable heat supply, independent of seasonal changes, ideal temperatures (70-90° C) for low-temperature desalination, cost-effectiveness with simultaneous power and water production, environmentally friendly with no emissions, versatile to meet energy demand across scales are some of main advantages of selection of geothermal energy for desalination. Some studies showed that geothermal energy for desalination is practical and effective, especially in areas facing water scarcity, strengthening water security. However, challenges persist, necessitating inventive solutions encompassing the pursuit of robust materials designed for high-temperature operation and streamlining energy conversion and integration processes. Other foreseen difficulties include the potential environmental impacts on geothermal reservoirs and the necessity for careful resource management to maintain a fair socio-economic equilibrium. Thus, the future research direction should be mainly focus on harnessing geothermal heat to drive the process, significantly reducing energy consumption and mitigating carbon emissions to potentially be employed across the world.&#x0D; &#x0D; Keywords: Carbon emission mitigation, Desalination, Energy conservation, Geothermal Energy, Water security&#x0D;

A comparative biomechanical study of the krackow suture technique with three common percutaneous suture techniques in the treatment of Achilles tendon ruptures

Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this article, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space, we refer to the set of adaptation options a self-adaptive system can select from to adapt at a given time based on the estimated quality properties of the adaptation options. A drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that the quality of the system may deteriorate, eventually, no adaptation option may satisfy the initial set of adaptation goals, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such a shift corresponds to a novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach aslifelong self-adaptation. The lifelong ML layer tracks the system and its environment, associates this knowledge with the current learning tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation. We validate the approach for a series of scenarios with a drift of adaptation spaces using the DeltaIoT exemplar.

A deep clustering framework for load pattern segmentation

Established bottom-up approaches for the characterization of nucleic acids (NAs) rely on the strand-cleavage activity of nucleotide-specific endonucleases to generate smaller oligonucleotides amenable to gas-phase sequencing. The complexity of these hydrolytic mixtures calls for the utilization of a front-end separation to facilitate full mass spectrometric (MS) characterization. This report explored the merits of microfluidic capillary zone electrophoresis (CZE) as a possible alternative to common liquid chromatography techniques. An oligonucleotide ladder was initially employed to investigate the roles of fundamental analyte features and experimental parameters in determining the outcome of CZE-MS analyses. The results demonstrated the ability to fully resolve the various rungs into discrete electrophoretic peaks with full-width half-height (FWHH) resolution that was visibly affected by the overall amount of material injected into the system. Analogous results were obtained from a digestion mixture prepared by treating yeast tRNAPhe (75 nt) with RNase T1, which provided several well-resolved peaks in spite of the increasing sample heterogeneity. The regular shapes of such peaks, however, belied the fact that most of them contained sets of comigrating species, as shown by the corresponding MS spectra. Even though it was not possible to segregate each species into an individual electrophoretic peak, the analysis still proved capable of unambiguously identifying a total of 29 hydrolytic products, which were sufficient to cover 96% of the tRNAPhe's sequence. Their masses accurately reflected the presence of modified nucleotides characteristic of this type of substrate. The analysis of a digestion mixture obtained from the 364 nt HIV-1 5'-UTR proved to be more challenging. The electropherogram displayed fewer well-resolved peaks and significantly greater incidence of product comigration. In this case, fractionating the highly heterogeneous mixture into discrete bands helped reduce signal suppression and detection bias. As a result, the corresponding MS data enabled the assignment of 248 products out of the possible 513 predicted from the 5'-UTR sequence, which afforded 100% sequence coverage. This figure represented a significant improvement over the 36 total products identified earlier under suboptimal conditions, which afforded only 57% coverage, or the 83 observed by direct infusion nanospray-MS (72%). These results provided a measure of the excellent potential of the technique to support the bottom-up characterization of progressively larger NA samples, such as putative NA therapeutics and mRNA vaccines.

Improved outcome models with denoising diffusion

In the present digital era, vast amounts of data are generated by millions of Internet users in the form of unstructured text documents. The clustering and organizing of text documents play a crucial role in the applications of data analysis and market research. In this research manuscript, a new modified version of metaheuristic-based optimization technique is proposed with k-means for clustering the text documents. In the initial phase, the input data are acquired from the three-benchmark databases such as Reuters-21578, 20-Newsgroup and British Broadcasting Corporation (BBC)-sport. Further, the data denoising is accomplished by using the common techniques: stemming, lemmatization, tokenization, and stop word removal. In addition to this, the denoised data are transformed into feature vectors by utilizing Term Frequency (TF)-Inverse Document Frequency (IDF) technique. The computed feature vectors are given to the Modified Particle Swarm Optimization (MPSO) with k-means to group the closely related text documents by minimizing the similarity in different clusters. The experimental examination showed that the proposed MPSO with k-means model achieved accuracy of 0.85, 0.85 and 0.86 on the Reuters-21578, 20-Newsgroup and BBC-sport databases, which are superior to the comparative models.

Carbon-based single-atom catalysts (SACs) have attracted tremendous interest in heterogeneous catalysis. However, the common electric heating techniques to produce carbon-based SACs usually suffer from prolonged heating time and tedious operations. Herein, a general and facile microwave-assisted rapid pyrolysis method is developed to afford carbon-based SACs within 3 min without inert gas protection. The obtained carbon-based SACs present high porosity and comparable carbonization degree to those obtained by electric heating techniques. Specifically, the single-atom Ni implanted N-doped carbon (Ni1 -N-C) derived from a Ni-doped metal-organic framework (Ni-ZIF-8) exhibits remarkable CO Faradaic efficiency (96 %) with a substantial CO partial current density (jCO ) up to 1.06 A/cm2 in CO2 electroreduction, far superior to the counterpart obtained by traditional pyrolysis with electric heating. Mechanism investigations reveal that the resulting Ni1 -N-C presents abundant defective sites and mesoporous structure, greatly facilitating CO2 adsorption and mass transfer. This work establishes a versatile approach to rapid and large-scale synthesis of SACs as well as other carbon-based materials for efficient catalysis.

Abstract Carbon‐based single‐atom catalysts (SACs) have attracted tremendous interest in heterogeneous catalysis. However, the common electric heating techniques to produce carbon‐based SACs usually suffer from prolonged heating time and tedious operations. Herein, a general and facile microwave‐assisted rapid pyrolysis method is developed to afford carbon‐based SACs within 3 min without inert gas protection. The obtained carbon‐based SACs present high porosity and comparable carbonization degree to those obtained by electric heating techniques. Specifically, the single‐atom Ni implanted N‐doped carbon (Ni1−N−C) derived from a Ni‐doped metal–organic framework (Ni‐ZIF‐8) exhibits remarkable CO Faradaic efficiency (96 %) with a substantial CO partial current density (jCO) up to 1.06 A/cm2 in CO2 electroreduction, far superior to the counterpart obtained by traditional pyrolysis with electric heating. Mechanism investigations reveal that the resulting Ni1−N−C presents abundant defective sites and mesoporous structure, greatly facilitating CO2 adsorption and mass transfer. This work establishes a versatile approach to rapid and large‐scale synthesis of SACs as well as other carbon‐based materials for efficient catalysis.