Entire Space Research Articles

Random Forest Modeling of Acute Toxicity in Anal Cancer: Effects of Peritoneal Cavity Contouring Approaches on Model Performance

Our purpose was to analyze the effect on gastrointestinal (GI) toxicity models when their dose-volume metrics predictors are derived from segmentations of the peritoneal cavity after different contouring approaches. A random forest machine learning approach was used to predict acute grade ≥3 GI toxicity from dose-volume metrics and clinicopathologic factors for 246 patients (toxicity incidence=9.5%) treated with definitive chemoradiation for squamous cell carcinoma of the anus. Three types of random forest models were constructed based on different bowel bag segmentation approaches: (1) physician-delineated after Radiation Therapy Oncology Group (RTOG) guidelines, (2) autosegmented by a deep learning model (nnU-Net) following RTOG guidelines, and (3) autosegmented but spanning the entire bowel space. Each model type was evaluated using repeated cross-validation (100 iterations; 50%/50% training/test split). The performance of the models was assessed using area under the precision-recall curve (AUPRC) and the receiver operating characteristic curve (AUROCC), as well as optimal F1 score. When following RTOG guidelines, the models based on the nnU-Net auto segmentations (mean values: AUROCC, 0.71 ± 0.07; AUPRC, 0.42 ± 0.09; F1 score, 0.46 ± 0.08) significantly outperformed (P < .001) those based on the physician-delineated contours (mean values: AUROCC, 0.67 ± 0.07; AUPRC, 0.34 ± 0.08; F1 score, 0.36 ± 0.07). When spanning the entire bowel space, the performance of the autosegmentation models improved considerably (mean values: AUROCC, 0.87 ± 0.05; AUPRC, 0.70 ± 0.09; F1 score, 0.68 ± 0.09). Random forest models were superior at predicting acute grade ≥3 GI toxicity when based on RTOG-defined bowel bag autosegmentations rather than physician-delineated contours. Models based on autosegmentations spanning the entire bowel space show further considerable improvement in model performance. The results of this study should be further validated using an external data set.

Fake and Post-Truth News in the Information Agenda in the Context of the First “Wave” of the Coronavirus Pandemic (COVID-19)

The article argues the synchronized processes of diffusion of misinformation (fakeization) and spread of post-truth news in the information agenda, disposed in the context of the first “wave” of the coronavirus (COVID-19) pandemic. The methods of case study, discourse analysis, and comparative analysis are applied. The empirical base is the materials of television channels (Channel One, Rossiya 1, NTV) and social networks (VKontakte and banned on the territory of the Russian Federation Facebook, Instagram). Timeline of the study – March-June 2020. The outcomes of the study demonstrate that the above processes were due to the problematization simultaneously with the “high and low threshold” – the spread of this social precedent to the entire social space and complete lack of such experience among individuals causes social disorientation and particularly manipulative susceptibility of society – and due to the differentiated motives of the actors. Based on the author's concept of pseudo-news, it is determined that fake is a product of mass consciousness, and post-truth – a result of the planned and deliberate tactics implemented by the powers that control media. Inspiring the processes of fakeization and post-truthization of the information agenda correlated with stable narratives of the main communication channels (the Internet and television), strengthening the established media picture, thereby constituting the qualification of conditional “heroes” and “enemies” in public opinion.

“Putnian Monastery and Community” as viewed by Dimitrie Dan

The article reveals the content of the monograph of the famous Bukovyna scholar Dimitrie Dan (1856–1927), dedicated to the Putnyan Monastery, an Orthodox shrine from the southern part of Bukovyna. In addition, the goal of the research is to translate a part of the monograph devoted to the ethnography of the village of Putnia. The relevance of the research is realized by the extreme interest of the inhabitants of the northern part of Bukovyna and other regions of Ukraine in the monastery from the neighbouring Suchav County of Romania. The in-depth analysis of the monograph and the translation of a certain fragment from it represent the novelty of the research. The author with utmost diligence revealed the existing state of the monastery and commune (community) at that time, revealed their history, described the appearance of the monastery and parish churches and the village, the burials of the founders of the holy monastery, looked under the tombstones of these burials, describing their contents like an archaeologist. He did not neglect the decoration and decoration of the monastery. He described with palpable love the manuscript and printed book fund of the monastery and the Desert (Sihăstria); the traditional culture of the Romanians of Putna, characterized the literary and cultural life that raged in the monastery for centuries. The stone cell and the Desert are described separately. Many documents related to monastic land ownership were published in the appendices. Conclusions. So, we can state that D. Dan's work “Putnian Monastery and Community with two Appendices” is a complete, comprehensive monograph that reveals the historical, geographical, economic and ethnographic features of one of the most significant monasteries of Bucovina and the entire Romanian ethnic space. Some details from the past of this holy abode (opening of the graves of the founders of the monastery or the transfer of the Dragoš-vode church to Putna) were forgotten over time and were known only to narrow specialists. At the same time, the ethnographic essay of the Putnia community made public the unique rites, rituals and texts of fortune-telling, which demonstrates the special skill of D. Dan as an ethnologist. The documents presented in the two appendices of the book can still be useful to local historians researching the history of their native village. In this work, all the positive qualities of his writing talent were fully manifested, such as good knowledge of sources, mastery of the method of synthesis, expressive and condensed text, clarity of ethnopsychological reflection, etc

Sol-Gel-Controlled Size and Morphology of Mesoporous Silica Microspheres Using Hard Templates.

Mesoporous silica microspheres (MPSMs) represent a promising material as a stationary phase for HPLC separations. The use of hard templates provides a preparation strategy for producing such monodisperse silica microspheres. Here, 15 MPSMs were systematically synthesized by varying the sol-gel reaction parameters of water-to-precursor ratio and ammonia concentration in the presence of a porous p(GMA-co-EDMA) polymeric hard template. Changing the sol-gel process factors resulted in a wide range of MPSMs with varying particle sizes from smaller than one to several micrometers. The application of response surface methodology allowed to derive quantitative predictive models based on the process factor effects on particle size, pore size, pore volume, and specific surface area of the MPSMs. A narrow size distribution of the silica particles was maintained over the entire experimental space. Two larger-scale batches of MPSMs were prepared, and the particles were functionalized with trimethoxy(octadecyl) silane for the application as stationary phase in reversed-phases liquid chromatography. The separation of proteins and amino acids was successfully accomplished, and the effect of the pore properties of the silica particles on separation was demonstrated.

Artificial intelligence inspired design of non-isothermal aging for γ–γ′ two-phase, Ni–Al alloys

In this paper, a state-of-the-art Artificial Intelligence (AI) technique is used for a precipitation hardening of Ni-based alloy to predict more flexible non-isothermal aging (NIA) and to examine the possible routes for the enhancement in strength that may be practically achieved. Additionally, AI is used to integrate with Materials Integration by Network Technology, which is a computational workflow utilized to model the microstructure evolution and evaluate the 0.2% proof stress for isothermal aging and NIA. As a result, it is possible to find enhanced 0.2% proof stress for NIA for a fixed time of 10 min compared to the isothermal aging benchmark. The entire search space for aging scheduling was ~ 3 billion. Out of 1620 NIA schedules, we succeeded in designing the 110 NIA schedules that outperformed the isothermal aging benchmark. Interestingly, it is found that early-stage high-temperature aging for a shorter time increases the γ′ precipitate size up to the critical size and later aging at lower temperature increases the γ′ fraction with no anomalous change in γ′ size. Therefore, employing this essence from AI, we designed an optimum aging route in which we attained an outperformed 0.2% proof stress to AI-designed NIA routes.

Reducing uncertainties in hydromechanical modeling with a recently developed Rosetta 3 podeotransfer function

Stability analysis of unsaturated landslide deposits requires reliable estimates of soil moisture and pore water pressure. However, modeled soil moisture and pore water pressure contain substantial uncertainties due to imperfect information on soil hydraulic properties. Due to the relatively high dimensionality, commonly used parameter optimization strategies can be significantly affected by equifinality problems. This study investigates the effectiveness of reducing parameter estimation dimensionality using soil pedo-transfer functions. Specifically, we first estimated soil hydraulic parameters using the traditional Generalized Likelihood Uncertainty Estimation (GLUE) method, with parameters randomly drawn from the entire space (refer to as GLUE-random). In a second strategy, we use the Rosetta 3 pedotransfer function to constrain soil hydraulic parameters (refer to as GLUE-Rosetta). The two methods were tested in a typical landslide deposit with in-situ measured soil moisture dynamics for inverse modeling. The GLUE-random estimated soil hydraulic parameters contained substantial uncertainties –resulting in poorly constrained soil water retention curves (SWCC) and hydraulic conductivity functions (HCF). As a result, the uncertainty bands of pore water pressure and slope stability can cross values with several orders of magnitudes. In contrast, GLUE-Rosetta provided well-constrained SWCC and HCF, which significantly reduce the uncertainties in pore water pressure and slope stability estimates. These results suggest that the Rosetta 3 pedotransfer function can significantly improve the reliability of soil hydraulic parameters by reducing the dimensionality of the optimization problem and high-quality prior information of soil hydraulic properties. In conclusion, Rosetta 3 can enhance the reliability of soil parameters estimates and the reliability of subsurface hydrology, which may benefit the development of landslide early-warning systems.

Dynamic Human–Robot Collision Risk Based on Octree Representation

The automation of manufacturing applications where humans and robots operate in a shared environment imposes new challenges for presenting the operator’s safety and robot’s efficiency. Common solutions relying on isolating the robots’ workspace from human access during their operation are not applicable for HRI. This paper presents an extended reality-based method to enhance human cognitive awareness of the potential risk due to dynamic robot behavior towards safe human–robot collaborative manufacturing operations. A dynamic and state-aware occupancy probability map indicating the forthcoming risk of human–robot accidental collision in the 3D workspace of the robot is introduced. It is determined using octrees and is rendered in a virtual or augmented environment using Unity 3D. A combined framework allows the generation of both static zones (taking into consideration the entire configuration space of the robot) and dynamic zones (generated in real time by fetching the occupancy data corresponding to the robot’s current configuration), which can be utilized for short-term collision risk prediction. This method is then applied in a virtual environment of the workspace of an industrial robotic arm, and we also include the necessary technical adjustments for the method to be applied in an AR setting.

On Consistent Entropy-Regularized k-Means Clustering With Feature Weight Learning: Algorithm and Statistical Analyses.

Clusters in real data are often restricted to low-dimensional subspaces rather than the entire feature space. Recent approaches to circumvent this difficulty are often computationally inefficient and lack theoretical justification in terms of their large-sample behavior. This article deals with the problem by introducing an entropy incentive term to efficiently learn the feature importance within the framework of center-based clustering. A scalable block-coordinate descent algorithm, with closed-form updates, is incorporated to minimize the proposed objective function. We establish theoretical guarantees on our method by Vapnik-Chervonenkis (VC) theory to establish strong consistency along with uniform concentration bounds. The merits of our method are showcased through detailed experimental analysis on toy examples as well as real data clustering benchmarks.

Stiefel-Whitney topological charges in a three-dimensional acoustic nodal-line crystal

Band topology of materials describes the extent Bloch wavefunctions are twisted in momentum space. Such descriptions rely on a set of topological invariants, generally referred to as topological charges, which form a characteristic class in the mathematical structure of fiber bundles associated with the Bloch wavefunctions. For example, the celebrated Chern number and its variants belong to the Chern class, characterizing topological charges for complex Bloch wavefunctions. Nevertheless, under the space-time inversion symmetry, Bloch wavefunctions can be purely real in the entire momentum space; consequently, their topological classification does not fall into the Chern class, but requires another characteristic class known as the Stiefel-Whitney class. Here, in a three-dimensional acoustic crystal, we demonstrate a topological nodal-line semimetal that is characterized by a doublet of topological charges, the first and second Stiefel-Whitney numbers, simultaneously. Such a doubly charged nodal line gives rise to a doubled bulk-boundary correspondence—while the first Stiefel-Whitney number induces ordinary drumhead states of the nodal line, the second Stiefel-Whitney number supports hinge Fermi arc states at odd inversion-related pairs of hinges. These results experimentally validate the two Stiefel-Whitney topological charges and demonstrate their unique bulk-boundary correspondence in a physical system.

PH-ORAM: An efficient persistent ORAM design for hybrid memory systems

The hybrid memory system (HMS), composed of DRAM and NVM, shows great potential for constructing large-scale, high-performance, and persistent memory systems. However, it is vulnerable to side-channel attacks that exploit the memory access pattern of programs to infer sensitive information. While Oblivious RAM (ORAM) is a general solution to defend against such attacks, it is not ready to be applied to HMSs. ORAM randomly distributes data across the entire memory space, causing the HMSs to lose the data persistence feature of NVM. An unexpected system crash will cause the DRAM data to be lost. Moreover, inconsistency may occur between metadata and data when the system crash interrupts ongoing ORAM operations. The inconsistency can cause the system to lose some NVM blocks after a crash reconstruction. How to effectively ensure data persistence and crash consistency in oblivious (or ORAM-protected) hybrid memory systems is still an open problem.This paper proposes PH-ORAM, an efficient ORAM design that provides a secure and persistent program execution environment on hybrid memory systems. First, we analyze the challenges of designing a persistent oblivious HMS and propose Persistent Duplication, an NVM-friendly data persistence scheme that does not require extra NVM writes or battery backup to persist DRAM data. Then, we analyze the consistency issues in NVM-based Ring ORAM systems and propose State-Aware stash management, a lightweight crash-consistent scheme that does not degrade system security and performance. Finally, we conduct cycle-accurate simulations to analyze the efficiency of PH-ORAM. Compared with the baseline Ring ORAM without any persistence support, PH-ORAM only increases the execution time by 0.6% on average.

REPAIRS: Gaussian Mixture Model-based Completion and Optimization of Partially Specified Systems

Most system optimization techniques focus on finding the values of the system components to achieve the best performance. Searching over all component values gives the search methodology the freedom to explore the entire design space to determine the best system configuration. However, real-world systems often require searching in a restricted space over only a subset of component values while freezing some of the components to fixed values. Rather than optimizing from scratch to search over the subset of components, incorporating the past simulation logs (search performed when all components were allowed to vary) enables the optimization mechanism to utilize knowledge from past system behavior. In addition, when the system gives the same response over different combinations of input values, the designer may prefer one combination over another. Furthermore, real-world data often contain errors. To avoid catastrophic consequences of making decisions based on incorrect data points, we need a mechanism to identify and correct the resulting error. We propose REPAIRS, a methodology to complete/optimize partially specified systems. It also performs data integrity checks and identifies/corrects errors after detecting an anomaly in the data. We use a Gaussian mixture model to learn the joint distribution of the system inputs and the corresponding output response (objectives/constraints). We use the learned model to complete a partially specified system where only a subset of the component values and/or the system response is specified. When the system response exhibits multiple modes (e.g., same response for different combinations of input values), REPAIRS determines the combinations of input values that correspond to the several modes. Using past simulation logs, it searches over various subsets of system inputs to improve the performance of the reference solution. We also present a framework for verifying the integrity of a given data instance. When the integrity check fails, we provide a mechanism to identify the error location and correct the error. REPAIRS provides an explanation for the decision it makes for the different use cases described in this article. We provide results of REPAIRS in the context of completion, partial optimization, and data integrity check of real-world systems. REPAIRS achieves a hypervolume that is better than that obtained using a baseline method by up to 50%. It successfully identifies the error location and predicts the correct value of the erroneous feature with an error less than 0.2%. It detects error locations with a mean accuracy of up to 95% even when three feature values have an error.

Artificial neural network-based optimization of baffle geometries for maximized heat transfer efficiency in microchannel heat sinks

Microchannel heat sinks (MCHSs) are widely utilized in various industries, including electronics, power systems, and aerospace, to dissipate heat generated by high-power components effectively. The efficient cooling of these devices is crucial for maintaining their optimal performance, reliability, and longevity. This research aimed to augment the overall efficiency of the MCHS by utilizing 25 baffles with varying geometric parameters. The aim was to identify the optimum geometry for these baffles using an Artificial Neural Network (ANN) model. The ANN model was applied to propose two optimal designs for the baffles, specifically targeting the maximum Nusselt number and overall performance of the MCHS. It was noticed that the vertical pitch of the baffles "d" had the most substantial influence on the device's behavior among the investigated geometric parameters. The higher value of "d" allowed the fluid to spread over the entire space of the heat sink rather than being confined to a specific area. This spreading effect enabled the fluid to contact all the baffles and channel walls, facilitating efficient heat transfer. The applied ANN model with R2 values of about 0.98 and lower values of MAE and RMSE successfully fitted the data. The findings of this study demonstrated that applying the specified input variables (horizontal pitch of 8.496 mm, vertical pitch of 5 mm, and attack angle of 210. 560°), as defined in the optimal overall design, in manufacturing of the baffles resulted in a remarkable enhancement of about 46% in the overall performance of the MCHS in comparison with the design proposed in the reference paper. The heat transfer improvement of the MCHS with baffles were due to the vortices in the baffles bring about chaotic advection and could greatly enhance the convective fluid mixing.

Controllable and Reproducible Growth of Transition Metal Dichalcogenides by Design of Experiments

AbstractControllable and reproducible synthesis of 2D materials is crucial for their future applications. Chemical vapor deposition (CVD) promises scalable and high‐quality growth of 2D materials. However, to optimize CVD growth, multiple parameters have to be carefully selected. Design of experiments (DoE) is a consistent and versatile tool to optimize all parameters simultaneously in a controlled way. This study exploits DoE statistical approaches to show how the CVD growth of transition metal dichalcogenides (TMDs) can be optimized, using tungsten disulfide as an example. A designed set of 29 different processes is used to cover the entire parameter space. The resulting growth output is characterized in terms of material morphology for factors such as single crystal size and continuous film size. The nonlinear model used to fit the output as a function of input parameters provides crucial insights into the nontrivial CVD process ensuring easy and systematic growth optimization. The predicted processes show successful optimization with respect to both the resulting material and the process stability. This powerful technique can be adapted for different setups and other TMD materials.

Skeleton-Based Human Action Recognition Based on Single Path One-Shot Neural Architecture Search

Skeleton-based human action recognition based on Neural Architecture Search (NAS.) adopts a one-shot NAS strategy. It improves the speed of evaluating candidate models in the search space through weight sharing, which has attracted significant attention. However, directly applying the one-shot NAS method for skeleton recognition requires training a super-net with a large search space that traverses various combinations of model parameters, which often leads to overly large network models and high computational costs. In addition, when training this super-net, the one-shot NAS needs to traverse the entire search space of the complete skeleton recognition task. Furthermore, the traditional method does not consider the optimization of the search strategy. As a result, a significant amount of search time is required to obtain a better skeleton recognition network model. A more efficient weighting model, a NAS skeleton recognition model based on the Single Path One-shot (SNAS-GCN) strategy, is proposed to address the above challenges. First, to reduce the model search space, a simplified four-category search space is introduced to replace the mainstream multi-category search space. Second, to improve the model search efficiency, a single-path one-shot approach is introduced, through which the model randomly samples one architecture at each step of the search training optimization. Finally, an adaptive Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is proposed to obtain a candidate structure of the perfect model automatically. With these three steps, the entire network architecture of the recognition model (and its weights) is fully and equally trained significantly. The search and training costs will be greatly reduced. The search-out model is trained by the NTU-RGB + D and Kinetics datasets to evaluate the performance of the proposed model’s search strategy. The experimental results show that the search time of the proposed method in this paper is 0.3 times longer than that of the state-of-the-art method. Meanwhile, the recognition accuracy is roughly comparable compared to that of the SOTA NAS-GCN method.

Symmetries of κ-Minkowski space-time: a possibility of exotic momentum space geometry?

The quest for a quantum gravity phenomenology has inspired a quantum notion of space-time, which motivates us to study the fate of the relativistic symmetries of a particular model of quantum space-time, as well as its intimate connection with the plausible emergent curved “physical momentum space”. We here focus on the problem of Poincare symmetry of κ-Minkowski type non-commutative (quantum) space-time, where the Poincare algebra, on its own, remains undeformed, but in order to preserve the structure of the space-time non-commutative (NC) algebra, the actions of the algebra generators on the operator-valued space-time manifold must be enveloping algebra valued that lives in entire phase space i.e. the cotangent bundle on the space-time manifold (at classical level). Further, we constructed a model for a spin-less relativistic massive particle enjoying the deformed Poincare symmetry, using the first order form of geometric Lagrangian, that satisfies a new deformed dispersion relation and explored a feasible regime of a future Quantum Gravity theory in which the momentum space becomes curved. In this scenario there is only a mass scale (Planck mass mp), but no length scale. Finally, we relate the deformed mass shell to the geodesic distance in this curved momentum space, where the mass of the particle gets renormalized as a result of noncommutativity. We show, that under some circumstances, the Planck mass provides an upper bound for the observed renormalized mass.

MPILS: An Automatic Tuner for MILP Solvers

The parameter configuration problem consists of finding a parameter configuration that gives a particular algorithm the best performance. This paper introduces a new multi-phase tuner based on the iterated local search meta-heuristic. This tuner addresses the parameter configuration problem for deterministic MILP solvers that are used to solve challenging industrial optimization problems. Further, the proposed tuner offers a new search strategy based on three ideas. First, instead of tuning in the entire configuration space induced by the parameter set, the multi-phase tuner focuses on a small parameter pool that is dynamically enriched with new promising parameters. Second, it leverages the gathered knowledge during the search using statistical learning to forbid less promising parameter combinations. Third, it tunes on a single instance provided by earlier clustering of MILP instances. A computational study on the widely-used commercial solver CPLEX with instances from the MIPLIB library and a real large-scale optimization problem highlights the promising potential of the tuner.

On Isotropy of Multimodal Embeddings

Embeddings, i.e., vector representations of objects, such as texts, images, or graphs, play a key role in deep learning methodologies nowadays. Prior research has shown the importance of analyzing the isotropy of textual embeddings for transformer-based text encoders, such as the BERT model. Anisotropic word embeddings do not use the entire space, instead concentrating on a narrow cone in such a pretrained vector space, negatively affecting the performance of applications, such as textual semantic similarity. Transforming a vector space to optimize isotropy has been shown to be beneficial for improving performance in text processing tasks. This paper is the first comprehensive investigation of the distribution of multimodal embeddings using the example of OpenAI’s CLIP pretrained model. We aimed to deepen the understanding of the embedding space of multimodal embeddings, which has previously been unexplored in this respect, and study the impact on various end tasks. Our initial efforts were focused on measuring the alignment of image and text embedding distributions, with an emphasis on their isotropic properties. In addition, we evaluated several gradient-free approaches to enhance these properties, establishing their efficiency in improving the isotropy/alignment of the embeddings and, in certain cases, the zero-shot classification accuracy. Significantly, our analysis revealed that both CLIP and BERT models yielded embeddings situated within a cone immediately after initialization and preceding training. However, they were mostly isotropic in the local sense. We further extended our investigation to the structure of multilingual CLIP text embeddings, confirming that the observed characteristics were language-independent. By computing the few-shot classification accuracy and point-cloud metrics, we provide evidence of a strong correlation among multilingual embeddings. Embeddings transformation using the methods described in this article makes it easier to visualize embeddings. At the same time, multiple experiments that we conducted showed that, in regard to the transformed embeddings, the downstream tasks performance does not drop substantially (and sometimes is even improved). This means that one could obtain an easily visualizable embedding space, without substantially losing the quality of downstream tasks.

Graph neural network-based spatio-temporal indoor environment prediction and optimal control for central air-conditioning systems

Model-based optimal control has proven its effectiveness in optimizing the performance of central air-conditioning systems in terms of thermal comfort and energy efficiency. It was often assumed that temperature distribution in the entire air-conditioned space is uniform and can be represented by a single or averaged measurement in optimization. However, actual distribution in the air-conditioned space is usually uneven, which can affect thermal comfort and indoor air quality. The dynamics of air conditioning systems and their interactions with the built environment exist in both time and space domains. Conventional data-driven approaches typically concentrate on the temporal correlations only among building operation data, while neglecting the spatial correlations. This study proposed a spatio-temporal data-driven methodology for optimal control of central air conditioning systems, which aims to address the challenging issue of uneven spatial distributions of environmental parameters in air-conditioned space. The methodology consists of graph-based multi-source data integration, graph neural network-based indoor environment modeling and spatio-temporal model-based online optimal control. The proposed methodology is tested on real air handling unit-variable air volume (AHU-VAV) system in a high-rise office building through a cloud-based platform. Spatio-temporal data from building automation system (BAS), internet of things (IoT) devices and building information modeling (BIM) are integrated and organized as graph structure. Graph neural network models are developed for predicting the evolution of indoor air temperature distribution under different control settings. The developed graph neural network-recurrent neural network (GNN-RNN) model architectures show enhanced accuracy than conventional deep learning models (i.e., convolutional neural network-recurrent neural network (CNN-RNN), Dense-RNN). And the cloud-based online test demonstrates that the proposed model-based control strategy improves the percentage of time achieving Grade I thermal comfort from 36.5% (i.e., existing control strategy) to 81.3%.

Star-shaped incision technique for gingiva patients treated with implant-supported fixed prosthesis.

To evaluate the modified star-shaped incision on gingival sulcus for reducing horizontal food impaction around implant-supported restoration. Total 24 patients receiving bone-level implant placement were enrolled, a star-shaped incision was made on the gingiva sulcus before the placement of zirconia crown. Follow-up examination was carried out 3 and 6 months after final restoration, respectively. Assessment of soft tissue includes papilla height, modified plaque index, modified sulcus bleeding index, periodontal depth, gingival biotype and gingival margin level. Marginal bone level was measured on periapical radiographs. Only 1 patient complained about the horizontal food impaction. Both the mesial and distal papilla almost filled the entire proximal space, in good harmony with the adjacent papillae. No recession of the gingival margin was found around the crown even in the patients with thin gingival biotype. Other parameters of soft tissue including modified plaque index, modified sulcus bleeding index and periodontal depth remained low during the whole follow-up visit. The resorption of marginal crestal bone was less than 0.6 mm during the first 6 month, and there was no significant difference among baseline, 3-month and 6-month visit. The modified star-shaped incision on the gingiva sulcus maintained the gingival papilla height and reduced the occurrence of horizontal food impaction, and no recession of the gingiva margin was found around implant-supported restoration.

Neuromuscular control: from a biomechanist's perspective.

Understanding neural control of movement necessitates a collaborative approach between many disciplines, including biomechanics, neuroscience, and motor control. Biomechanics grounds us to the laws of physics that our musculoskeletal system must obey. Neuroscience reveals the inner workings of our nervous system that functions to control our body. Motor control investigates the coordinated motor behaviours we display when interacting with our environment. The combined efforts across the many disciplines aimed at understanding human movement has resulted in a rich and rapidly growing body of literature overflowing with theories, models, and experimental paradigms. As a result, gathering knowledge and drawing connections between the overlapping but seemingly disparate fields can be an overwhelming endeavour. This review paper evolved as a need for us to learn of the diverse perspectives underlying current understanding of neuromuscular control. The purpose of our review paper is to integrate ideas from biomechanics, neuroscience, and motor control to better understand how we voluntarily control our muscles. As biomechanists, we approach this paper starting from a biomechanical modelling framework. We first define the theoretical solutions (i.e., muscle activity patterns) that an individual could feasibly use to complete a motor task. The theoretical solutions will be compared to experimental findings and reveal that individuals display structured muscle activity patterns that do not span the entire theoretical solution space. Prevalent neuromuscular control theories will be discussed in length, highlighting optimality, probabilistic principles, and neuromechanical constraints, that may guide individuals to families of muscle activity solutions within what is theoretically possible. Our intention is for this paper to serve as a primer for the neuromuscular control scientific community by introducing and integrating many of the ideas common across disciplines today, as well as inspire future work to improve the representation of neural control in biomechanical models.