Low Deviation Research Articles

Strategic Deviation and Corporate Tax Avoidance: A Risk Management Perspective

We examine the association between strategic deviation—defined as the deviation of firms’ resource allocation from that of industry peers—and corporate tax avoidance. By combining the agency perspective with the risk aspect, we argue that managers of firms with high strategic deviation avoid tax compared with those of firms with low strategic deviation. High-strategic-deviant firms who avoid tax are likely to face the risk of compromising firm value. Based on a large sample of 40,168 US firm-year observations for the period 1987–2020, we find evidence supporting our hypothesis. A series of robustness tests validates our main finding. We further provide evidence to suggest that the positive association between strategic deviation and tax avoidance is stronger for deviant firms with high financial constraints, low institutional ownership, firms operating in more competitive markets, and procuring higher auditor provided tax services from incumbent auditors. Importantly, we show that the capital market penalises tax avoidance strategies undertaken by the deviant firms.

Isotope engineering achieved by local coordination design in Ti-Pd co-doped ZrCo-based alloys

Deuterium/Tritium (D/T) handling in defined proportions are pivotal to maintain steady-state operation for fusion reactors. However, the hydrogen isotope effect in metal-hydrogen systems always disturbs precise D/T ratio control. Here, we reveal the dominance of kinetic isotope effect during desorption. To reconcile the thermodynamic stability and isotope effect, we demonstrate a quantitative indicator of Tgap and further a local coordination design strategy that comprises thermodynamic destabilization with vibration enhancement of interstitial isotopes for isotope engineering. Based on theoretical screening analysis, an optimized Ti-Pd co-doped Zr0.8Ti0.2Co0.8Pd0.2 alloy is designed and prepared. Compared to ZrCo alloy, the optimal alloy enables consistent isotope delivery together with a three-fold lower Tgap, a five-fold lower energy barrier difference, a one-third lower isotopic composition deviation during desorption and an over two-fold higher cycling capacity. This work provides insights into the interaction between alloy and hydrogen isotopes, thus opening up feasible approaches to support high-performance fusion reactors.

The antihyperglycemic potential of pyrazolobenzothiazine 1, 1-dioxide novel derivative in mice using integrated molecular pharmacological approach

Diabetes Mellitus is a metabolic disease characterized by elevated blood sugar levels caused by inadequate insulin production, which subsequently leads to hyperglycemia. This study was aimed to investigate the antidiabetic potential of pyrazolobenzothiazine derivatives in silico, in vitro, and in vivo. Molecular docking of pyrazolobenzothiazine derivatives was performed against α-glucosidase and α-amylase and compounds were selected based on docking score, bonding interactions and low root mean square deviation (RMSD). Enzyme inhibition assay against α-glucosidase and α-amylase was performed in vitro using p-nitrophenyl-α-D-glucopyranoside (PNPG) and starch substrate. Synthetic compound pyrazolobenzothiazine (S1) exhibited minimal conformational changes during the 100 ns MD simulation run. S1 also revealed effective IC50 values for α-glucosidase (3.91 µM) and α-amylase (8.89 µM) and an enzyme kinetic study showed low ki (− 0.186 µM, − 1.267 µM) and ki′ (− 0.691 µM, − 1.78 µM) values with the competitive type of inhibition for both enzymes α-glucosidase and α-amylase, respectively. Moreover, studies were conducted to check the effect of the synthetic compound in a mouse model. A low necrosis rate was observed in the liver, kidney, and pancreas through histology analysis performed on mice. Compound S1 also exhibited a good biochemical profile with lower sugar level (110–115 mg/dL), increased insulin level (25–30 μM/L), and low level of cholesterol (85 mg/dL) and creatinine (0.6 mg/dL) in blood. The treated mice group also exhibited a low % of glycated haemoglobin (3%). This study concludes that S1 is a new antidiabetic-agent that helps lower blood glucose levels and minimizes the complications associated with type-II diabetes.

Dental Implant Positioning Accuracy Using a Key or Keyless Static Fully Guided Surgical System: A Prospective Systematic Review and Meta-analysis.

To realize a systematic review with prospective meta-analysis aiming to compare the accuracy of static fully guided implant placement depending on the drill key modality (conventional drill key surgery systems vs newer keyless systems) and evaluate the impact of deviation factors. An electronic systematic search was conducted to identify prospective clinical trials matching inclusion criteria. The variables of interest were coronal global, apical, vertical, and angular deviations. The types of edentulism and surgical guide support were investigated as deviation factors. Meta-regression (mixed-effect model) was performed. Heterogeneity was assessed using Cochrane's I. test and interpretation thresholds. A total of 1,233 implants in 475 patients were analyzed (18 studies included). Coronal global deviation was significantly lower in the keyless group than in the key group (-0.36 mm; 95% CI -0.62, -0.09; P = .008). Angulation control of the keyless system was superior to that of the key system (-0.36 degrees; 95% CI -0.75, 0.02; P = .063). Nonsignificant differences were found between both groups in apical (P = .684) and vertical deviations (P = .958). Significant influence of the type of edentulism (single, partial, total) and surgical guide support (tooth, mucosa, bone) on the overall amount of coronal global, apical, and angular deviations was found (P < .001). The lowest deviations were found in partial edentulism and tooth-supported surgical guide groups. (P < .001). Keyless static fully guided surgical systems allowed significantly better control of coronal and angular deviations than conventional systems. The types of edentulism and surgical guide support seemed to influence the positioning accuracy.

Associations of postoperative outcomes with geriatric nutritional risk index after conventional and robotic-assisted total knee arthroplasty: a randomized controlled trial.

The association between postoperative outcomes of robotic-assisted total knee arthroplasty (RA-TKA) and nutrition status among elderly adults remained unclear. The authors aimed to evaluate these associations and provide a nutrition status reference for the surgical technique selection of TKA. In the present study, the authors used data from a multicenter, prospective, randomized controlled project, which recruited patients underwent TKA therapy. A total of 88 elderly adults (age ≥65 years old) were included in this study. Their preoperative and postoperative demographic data and radiographic parameters were collected. Clinical outcomes, including postoperative hip-knee-ankle (HKA) angle deviation, knee society score (KSS), 10cm visual analog scale, and so on, were observed and compared between the RA-TKA group and the conventional TKA group. Logistic regression was performed to adjust several covariates. In addition, according to the results of restricted cubic splines analyses, all participants were categorized into two groups with GNRI≤100 and GNRI >100 for further subgroup analyses. Our results showed despite having a lower postoperative HKA angle deviation, the RA-TKA group had a similar postoperative KSS score compared with the conventional TKA group in elderly adults. Among elderly patients with GNRI>100, RA-TKA group achieved significantly more accurate alignment (HKA deviation, P =0.039), but did not obtain more advanced postoperative KSS scores because of the compensatory effect of good nutrition status. However, among elderly patients with GNRI≤100, RA-TKA group had significantly higher postoperative KSS scores compared to the conventional TKA group ( P =0.025) and this association were not altered after adjustment for other covariates. Considering the clinical outcomes of conventional TKA may be more susceptible to the impact of nutrition status, elderly patients with GNRI≤100 seem to be an applicable population for RA-TKA, which is more stable and would gain significantly more clinical benefits compared with conventional TKA.

High-throughput thermoelectric materials screening by deep convolutional neural network with fused orbital field matrix and composition descriptors

Thermoelectric materials harvest waste heat and convert it into reusable electricity. Thermoelectrics are also widely used in inverse ways such as refrigerators and cooling electronics. However, most popular and known thermoelectric materials to date were proposed and found by intuition, mostly through experiments. Unfortunately, it is extremely time and resource consuming to synthesize and measure the thermoelectric properties through trial-and-error experiments. Here, we develop a convolutional neural network (CNN) classification model that utilizes the fused orbital field matrix and composition descriptors to screen a large pool of materials to discover new thermoelectric candidates with power factor higher than 10 μW/cm K2. The model used our own data generated by high-throughput density functional theory calculations coupled with ab initio scattering and transport package to obtain electronic transport properties without assuming constant relaxation time of electrons, which ensures more reliable electronic transport properties calculations than previous studies. The classification model was also compared to some traditional machine learning algorithms such as gradient boosting and random forest. We deployed the classification model on 3465 cubic dynamically stable structures with non-zero bandgap screened from Open Quantum Materials Database. We identified many high-performance thermoelectric materials with ZT &amp;gt; 1 or close to 1 across a wide temperature range from 300 to 700 K and for both n- and p-type doping with different doping concentrations. Moreover, our feature importance and maximal information coefficient analysis demonstrates two previously unreported material descriptors, namely, mean melting temperature and low average deviation of electronegativity, that are strongly correlated with power factor and thus provide a new route for quickly screening potential thermoelectrics with high success rate. Our deep CNN model with fused orbital field matrix and composition descriptors is very promising for screening high power factor thermoelectrics from large-scale hypothetical structures.

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

Alterations in paraffin wax crystal networks induced by asphaltenes and pour point depressants, investigated by atomic force microscopy

Wax crystallization in pipelines in cold environments is a key factor affecting flow assurance during crude oil production. Polar crude oil components, such as asphaltenes, and polymeric pour point depressants (PPDs) have been demonstrated to interact with wax during gelation and crystallization through complex formation during nucleation or crystal growth alteration which leads to co-crystallization. This study introduces a methodology based on atomic force microscopy (AFM) to characterize features of solid surfaces precipitated from model wax, wax-PPD, wax-asphaltene and asphaltene systems. Height data collected from AFM measurements enabled the quantification of modifications in crystal sizes and shapes induced by the presence of asphaltenes and PPDs. The 2D Fourier transform of the height was used to generate statistics for the frequency of height amplitude, revealing information about the pore size and the height patterns. A stiffness/elasticity analysis was used to calculate the Young Modulus of the upper layers. In wax and wax-asphaltene systems, two nano-scale layers with different Young Modulus profiles emerged. The nano-scale layer on the top was softer and corresponded to more amorphous wax crystal networks with trapped dissolved wax. The nano-scale layer on the bottom was stiffer, comprising of more crystalline structures, corresponding to wax crystals with lower deviations from full crystallinity, with properties independent of asphaltene concentration. A bilayer did not form in the presence of PPD, which prevented the formation of amorphous phases with trapped dissolved wax or gel. After a methodology for model systems was established, the focus moved to systems based on production wax i.e. recovered from deposits. This was complemented by a comparison between model precipitation conditions and precipitation by cold finger, resembling industrial processes. Results highlighted the effect of change in chemical composition on topography, elasticity and adhesion of the surface. AFM provided new insights into wax crystallization patterns in the presence of inhibitors, which can be used to quantify the extent of wax-inhibitor interactions and project it on production systems. The method could be used to explain macro-scale rheological properties of oils during flow assurance in the future.

Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm

Valorization of agricultural wastes is ongoing topic in industry. Determining the best conditions by artificial neural networks based optimization techniques is the key step to extract valuable compounds efficiently and to obtain high quality extracts. In this study, the response surface methodology (RSM)-desirability function (DF) and artificial neural network (ANN)-genetic algorithm (GA) approaches were compared in modeling and optimization the extraction parameters (temperature, time and ethanol concentration (ratio of ethanol to water, % v/v)) of phenolic compounds in pomegranate peels. The ANN-GA approach providing higher coefficient of determination and lower root mean square deviation showed better predictive capability than the RSM. The optimum time (81.4 min) and ethanol concentration (15.7%) of RSM-DF approach shifted to the lower levels (78.8 min and 15.3%) with the ANN-GA approach while the optimum temperature (54.0°C) shifted to a higher level (59.3°C). The use of these values provided total phenolic content of &gt;1000 mg GAE L-1 and the corresponding antioxidant activity was 11 mmol TE L-1. As a result, increasing temperature up to a critical level decreased the extraction time and ethanol concentration, and it was determined that higher time-temperature combinations must be used for the complete water-based extraction of phenolic compounds from plant wastes in comparison to ethanol-water based extraction.

Study on the bending and dimensional behavior of honeycomb latticed ONYX composite material

Engineering, architecture, and transportation all use honeycomb structures in various ways. Latticing, made possible by additive manufacturing (AM), may significantly speed up the creation of adaptable structures. The current study focuses on assessing the impact of various three-dimensional (3D)-printing features on the flexural behavior and dimensional studies of the honeycomb latticed micro-carbon fiber reinforced nylon (ONYX) material. The experiment is performed by varying the levels of 3D-printing features like layer thickness (LT), infill geometry (IG), build direction (BD), shell count (SC), and infill percentage (IP) to measure the bending strength, length deviation, and lattice surface morphology variation. The experimental results show that, the peak flexural strength of 79.84 MPa is attained with specimen fabricated at 0.1 mm LT, 50% IP, 0° BD, rectilinear IG, and SC of 3. And moreover, these respective 3D-printing feature levels resulted in an improved surface morphology on the latticed specimens. The length deviation results clearly depict that specimen fabricated at lower LT of 0.1 mm, higher SC of 3, rectilinear IG, 0° BD, and higher IP of 50% consequences in accurate profile with a lower length deviation of 0.117 mm. The fractography results clearly implies that the 0° oriented latticed ONYX composite results in a progressive fracture and results in higher bending stress. On the other hand, the 45° and 90° oriented latticed ONYX materials undergo tilted fracture and perpendicular mode of fracture, respectively. From that it is concluded that, the triangular-shaped honeycomb latticed ONYX materials are suitable for the development of brackets for the portable cameras, medical, and dental appliances like prosthetics for lightweight splints in postsurgery.

An investigation on using artificial intelligence models to predict crater wear of tungsten carbide tool

In this study, artificial intelligence (AI) tools were utilised to predict and analyse the progression of crater wear in cutting tools made of tungsten carbide during machining of aluminium 7075 alloy with a CNC lathe. The study investigated the impact of corner radius, feed rate, cutting speeds, and cut depth on the wear of the tools. Thirty experiments were conducted, with 24 used for training 11 independent AI models and the remaining 6 used for testing. This study stands out for its novelty as it pioneers the evaluation of 11 distinct AI models for the prediction of tool wear. With a high level of accuracy and a lower average deviation, the most effective model identified in this study was the gradient boosting model. By integrating AI algorithms into manufacturing processes, the monitoring of tool wear becomes more efficient, leading to reduce experiments, minimise testing costs, predict tool life, prevent failures, and boost productivity.

Probabilistic effect of non-potable mixing water on characteristic strength of concrete

Across the world, the acceptance criterion for concrete made with non-potable water (SA) is stipulated for mean strength with reference to similar concrete made with potable water (SP). The basis for the strength design of concrete is the characteristic strength. The effect of the acceptance criteria on the characteristic strength of SA and SP was examined using statistical simulations, for the first time. It was found that, compared with a two-sample set, using a three-sample set would improve the characteristic strength of SA. A higher allowable ratio between the means of SA and SP and a lower deviation of individual samples from the mean would improve the characteristic strength of SA. The effect of the coefficient of variation (CoV) of the original population on the characteristic strength of SA varied. On the one hand, a higher CoV reduced the characteristic strength of SP, thereby increasing the probability of SA strength being higher than SP strength. On the other, a higher CoV of SP meant a wider spread of the samples and that could increase the CoV of SA, thereby reducing the characteristic strength of SA. Probabilistic charts were developed for the characteristic strengths of SA and SP for different combinations of the aforementioned variables, with guidance for practical application.

Energy and economic analysis of building integrated photovoltaic thermal system: Seasonal dynamic modeling assisted with machine learning-aided method and multi-objective genetic optimization

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

The influence of the raster angle on the dimensional accuracy of FDM-printed PLA, PETG, and ABS tensile specimens

3D printing is a rapidly advancing method in digital manufacturing techniques and produces objects in layers. Fused Deposition Modelling (FDM) is a 3D printing technology where the material is melted in a hot nozzle and then placed on a build platform to create a prototype layer by layer. In this study, the effects of different raster angles (0°, 45°, 90°, 45°/-45°, 0°/90°) on dimensional accuracy for PLA, PETG and ABS materials produced using FDM were investigated. The results show that PETG generally shows higher dimensional deviations compared to PLA and ABS, and samples with a scan angle of 90° generally have lower deviation percentages than other angles. Width deviations (approximately 1.5% on average) were lower than thickness deviations (approximately 9.5% on average). Analysis of the cross-sectional areas shows that all samples are above the theoretical area (41.6 mm2). PETG samples with a scan angle of 45°/-45° exhibit the largest cross-sectional area (46.78 mm2), while ABS samples with a scan angle of 90° exhibit the smallest (45.46 mm2). This study is important to understand the impact of material selection and raster angle on dimensional accuracy, and it is recommended to account for cross-sectional deviations and calculate the stress based on the actual cross-sectional area to achieve more accurate results in applications requiring precise measurements. These data offer valuable information for those interested in 3D printing and its professionals and can lead to further research in this field, so that printing techniques can be further developed and product quality can be improved.

Assessing lateral thinking: validity, reliability, and universality using a novel verbal test

Lateral thinking, a key component of creative thinking, is crucial in problem-solving, scientific discovery, and everyday life. However, its assessment remains challenging due to its elusive nature. This study proposes and validates a novel verbal test to objectively measure lateral thinking. The test, consisting of triads of words with subtle associations, requires subjects to find commonalities between seemingly unrelated concepts. The study involves three groups: two student groups and one engineering group. Statistical analysis reveals that the test demonstrates validity, reliability, and representativeness. Notably, the probability distribution of the measured parameter shows low deviations from the normal law, suggesting that lateral thinking, like general intelligence, is a relatively universal personality trait. This work contributes to a deeper understanding of lateral thinking and provides a validated tool for its assessment, promoting creativity in education and various other fields.

Fabrication trueness and marginal quality of additively manufactured resin-based definitive laminate veneers with different restoration thicknesses

ObjectivesTo evaluate how restoration thickness (0.5 mm and 0.7 mm) affects the fabrication trueness of additively manufactured definitive resin-based laminate veneers, and to analyze the effect of restoration thickness and margin location on margin quality. MethodsTwo maxillary central incisors were prepared either for a 0.5 mm- or 0.7 mm-thick laminate veneer. After acquiring the partial-arch scans of each preparation, laminate veneers were designed and stored as reference data. By using these reference data, a total of 30 resin-based laminate veneers were additively manufactured (n = 15 per thickness). All veneers were digitized and stored as test data. The reference and test data were superimposed to calculate the root mean square values at overall, external, intaglio, and marginal surfaces. The margin quality at labial, incisal, mesial, and distal surfaces was evaluated. Fabrication trueness at each surface was analyzed with independent t-tests, while 2-way analysis of variance was used to analyze the effect of thickness and margin location on margin quality (α = 0.05). ResultsRegardless of the evaluated surface, 0.7 mm-thick veneers had lower deviations (P < 0.001). Only the margin location (P < 0.001) affected the margin quality as labial margins had the lowest quality (P < 0.001). ConclusionRestoration thickness affected the fabrication trueness of resin-based laminate veneers as 0.7 mm-thick veneers had significantly higher trueness. However, restoration thickness did not affect the margin quality and labial margins had the lowest quality. Clinical significanceLaminate veneers fabricated by using tested urethane-based acrylic resin may require less adjustment when fabricated in 0.7 mm thickness. However, marginal integrity issues may be encountered at the labial surface.

Unravelling the link between SARS-CoV-2 mutation frequencies, patient comorbidities, and structural dynamics.

Genomic surveillance is crucial for tracking emergence and spread of novel variants of pathogens, such as SARS-CoV-2, to inform public health interventions and to enforce control measures. However, in some settings especially in low- and middle- income counties, where sequencing platforms are limited, only certain patients get to be selected for sequencing surveillance. Here, we show that patients with multiple comorbidities potentially harbour SARS-CoV-2 with higher mutation rates and thus deserve more attention for genomic surveillance. The relationship between the patient comorbidities, and type of amino acid mutations was assessed. Correlation analysis showed that there was a significant tendency for mutations to occur within the ORF1a region for patients with higher number of comorbidities. Frequency analysis of the amino acid substitution within ORF1a showed that nsp3 P822L of the PLpro protease was one of the highest occurring mutations. Using molecular dynamics, we simulated that the P822L mutation in PLpro represents a system with lower Root Mean Square Deviation (RMSD) fluctuations, and consistent Radius of gyration (Rg), Solvent Accessible Surface Area (SASA) values-indicate a much stabler protein than the wildtype. The outcome of this study will help determine the relationship between the clinical status of a patient and the mutations of the infecting SARS-CoV-2 virus.

Transfer learning-driven artificial intelligence model for glass transition temperature estimation of molecular glass formers mixtures

Predicting binary mixtures' glass transition temperature (Tg) is crucial in various fields, particularly for industrial materials affected by this property during production processes and in service-life. On the other hand, from the fundamental point of view, this predictive capability is relevant for understanding the chemical interactions between the two components and how this affects the Tg of the mixture. In this sense, some models provide different approaches for describing the Tg of the mixture. Among them, the Gordon-Taylor approach has been widely used since it only relies on the relationship between the Tg of the pure components, their weight fraction, and only one fitting parameter. Although simple, this approach still requires measurements of Tg of the pure components and at least some intermediated composition for the fitting procedure. In a previous work, our research has focused on neural networks methods for predicting Tg values directly from the chemical structure of monomers and molecules, but the scarcity of experimental data for binary mixtures limits the application of a similar approach. To address this problem, we propose to use in this work a transfer learning method that relays on the previous acquired knowledge of the chemical structure - Tg relationship, for the prediction of the Tg of the binary mixtures. Therefore, pure component characteristics are derived from chemical fingerprints originated in a pre-trained network, and enables a training process focused on their behavior within the mixtures. This approach successfully estimated K with very low deviations, even allowing for the exploration of the embedded chemical structure's relation to previously unknown mixtures.

Flexible-Arranged Biomimetic Array Integrated with Parallel Entropy-Driven Circuits for Ultrasensitive, Multiple, and Reliable Detection of Cancer-Related MicroRNAs.

With the emergence of microRNA (miRNA) as a promising biomarker in cancer diagnosis, it is significant to develop multiple analyses of miRNAs. However, it still faces difficulties in ensuring the sensitivity and accuracy during multiplex detection owing to the low abundance and experimental deviation of miRNAs. In this work, a flexible-arranged biomimetic array integrated with parallel entropy-driven circuits (EDCs) was developed for ultrasensitive, multiplex, reliable, and high-throughput detection of miRNAs. The biomimetic array was fabricated by arrangement of various photonic crystals (PCs) for adjustable photonic band gaps (PBGs) and specific fluorescence enhancement. Meanwhile, two cancer-related miRNAs and one reference miRNA were introduced as multiple analytes as a proof-of-concept. The parallel EDCs with negligible crosstalk were designed based on the modular property. Because of the one-to-one match between the emitted fluorescence of parallel EDCs and the PBGs of the flexible-arranged biomimetic array, the generated fluorescence signal triggered by target miRNAs can be enhanced on the corresponding domain of the array. Furthermore, the amplified signal of the array was detected with high-throughput scanning, which could reveal specific information on cancer-related miRNAs as well as reference miRNA, enhancing the abundance and reliability of the analysis. The proposed array has the merits of a modular design, flexible deployment, simple operation (nonenzymatic and isothermal), improved accuracy, high sensitivity, and multiplex analysis, showing potential in disease diagnosis.

How does orthotic walker boot design influence lower limb and trunk function during gait?

Background: Undesirable lower limb gait deviations have previously been reported when wearing orthotic walker boots; therefore, there is a need to optimize orthotic walker boot designs to facilitate normal gait. Objective: This study explored the biomechanical effects of 2 designs of orthotic walker boot on the lower limb and trunk compared with usual footwear. Study design: A repeated measures analysis of variance was used to evaluate selected kinematic and kinetic variables under different walking conditions. Methods: Sixteen healthy participants walked in 3 conditions using: Walker A (Airselect Elite, Enovis), Walker B (Townsend XLR8 Series Walker, Thuasne, France), and a usual shoe. A 10-camera motion analysis system and 4 force plates were used to collect kinematic and kinetic data. Results: Gait speed was significantly slower in both orthotic walker boots, and there was significantly decreased ankle range of motion, which is their primary function. Significant deviations in normal knee and hip kinematics and kinetics, shank-to-vertical angle, and pelvic and trunk movements were noted with both walker boots, with the greatest deviations from the shoe condition observed in Walker B. Recline and incline shank angular velocities showed the greatest differences in Walker B, which could be associated with adverse knee joint moments and a significantly greater perceived ease of walking in Walker A. Conclusions: Orthotic walker boot design significantly affects walking mechanics. Orthotic walkers with greater forefoot rocker profiles and inclined vertical shank angles may at least partly mitigate known gait deviations when wearing orthotic walkers.