Related Techniques Research Articles

A Comprehensive Survey on Automated Machine Learning for Recommendations

Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user’s interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non-linear relationships between users and items. Despite their advancements, DRS models, like other deep learning models, employ sophisticated neural network architectures and other vital components that are typically designed and tuned by human experts. This article will give a comprehensive summary of automated machine learning (AutoML) for developing DRS models. We first provide an overview of AutoML for DRS models and the related techniques. Then we discuss the state-of-the-art AutoML approaches that automate the feature selection, feature embeddings, feature interactions, and model training in DRS. We point out that the existing AutoML-based recommender systems are developing to a multi-component joint search with abstract search space and efficient search algorithm. Finally, we discuss appealing research directions and summarize the survey.

Enhancing Electrochemical Properties of Walnut Shell Activated Carbon with Embedded MnO Clusters for Supercapacitor Applications

AbstractActivated carbon (AC) materials from renewable sources are widely used in electrochemical applications due to their well‐known high surface area. However, their application as electrode material in double‐layer electrochemical devices may be limited due to their relatively low electrical conductivity and lightweight. To overcome these limitations, the incorporation of pseudocapacitance metal oxide nanoparticles is an optimum approach. These nanoparticles can provide a second energy storage mechanism to the composite, mitigating the loss of surface area associated with their incorporation. As a result, the composite material is endowed with increased conductivity and higher density, making it more suitable for practical implementation in real devices. In this study, we have incorporated a fine dispersion of 1 % of MnO clusters into a highly porous activated carbon synthesized from walnut shells (WAC). The high‐resolution electron microscopy studies, combined with their related analytical techniques, allow us to determine the presence of the cluster within the matrix carbon precisely. The resulting MnO@WAC composite demonstrated significantly improved capacitive behavior compared with the WAC material, with increased volumetric capacitance and higher charge retention at higher current densities. The composite‘s electrochemical performance suggests its potential as a promising electrode material for supercapacitors, addressing drawbacks associated with traditional AC materials.

Position Versus Class


 
 
 Positional notation and related numerical manipulation techniques of Indian origin were introduced to Europe during the twelfth century through Arabic mediation and vividly described by Fibonacci as modus Indorum, the method of the Indians. This article aims to juxtapose Sanskrit and Latin texts to highlight the connections and differences between matrix and reflection in a complex cultural process of diffusion and assimilation. With reference to positional notation, this contribution examines a conceptual distinction between the graphical notion of position and a logical-classificatory principle hitherto perhaps not fully recognized, although present in the texts of both traditions. The thesis of a consequent diagrammatic conception of positional notation in its origins, as a system of rules relating graphical position and order of magnitude, is also advanced.
 
 

Metal-Organic Frameworks Based Sensor Platforms for Rapid Detection of Contaminants in Wastewater.

Metal-organic frameworks (MOFs) are a type of multifunctional material with organic-inorganic doped metal complexes that have a lot of unsaturated metal sites and a consistent network structure. MOFs work has great performance for enhancing the mass transfer, signal, and sensitivity as well as analyte enrichment. This study highlights the recent advancements of MOFs-based sensors for pollutant detection in a water environment and summarizes the effect of various synthetic materials on the performance of MOFs-based sensors. The related challenges and optimization techniques have been discussed. Then the research results of various MOFs sensors in the detection of wastewater pollutants are analyzed. Finally, the challenges facing MOFs-based water sensor development and the outlook for future research are discussed.

Knowledge-based recommender systems: overview and research directions.

Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.

Advances in the pilot point inverse method: Où En Sommes-Nous maintenant?

At a conference some years ago, one of the attendees came up to Ghislain de Marsily and asked, “Excuse me, but aren’t you the de Marsily who developed the Pilot Point Method? It is an honor to meet you.” His response highlights one of Ghislain’s greatest qualities, his humility: “Yes I am, thank you, you are very kind, but that was a very long time ago.” Since Ghislain de Marsily first developed the Pilot Point Method (PPM) in 1978, its development and use has grown significantly in applied decision-support modeling settings including hydrogeology, as well as in other industries, e.g., petroleum reservoir engineering. A technique that was once confined to academic realms, the PPM is now widely accepted as one of the industry pillars of inversion and uncertainty quantification for predictive groundwater modeling. Herein, we provide an update to de Marsily’s paper entitled “Four Decades of Inverse Problems in Hydrogeology” [De Marsily et al., 2000], but with a particular focus on the incredible adoption and advancement of de Marsily’s PPM and related inverse techniques over the last twenty years in the field of predictive groundwater modeling.

Exploring deep learning techniques for wild animal behaviour classification using animal‐borne accelerometers

Abstract Machine learning‐based behaviour classification using acceleration data is a powerful tool in bio‐logging research. Deep learning architectures such as convolutional neural networks (CNN), long short‐term memory (LSTM) and self‐attention mechanism as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration‐based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached and complexity in data due to complex animal‐specific behaviours, which may have limited the application of deep learning techniques in this area. To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup and pre‐training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state‐of‐the‐art deep learning model architectures. Data augmentation improved the overall model performance when one of the various techniques (none, scaling, jittering, permutation, time‐warping and rotation) was randomly applied to each data during mini‐batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre‐training with unlabelled data did not improve model performance. The state‐of‐the‐art deep learning models, including a model consisting of four CNN layers, an LSTM layer and a multi‐head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features. Our experiments showed that deep learning techniques are promising for acceleration‐based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning and self‐supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time‐series sensor data.

Marketing’s Debt to World War I

World War I was a global conflict produced blood filled trenches, battlefields of dead, and mangled soldiers. Some may feel that this war has little to do with the field of marketing. However, marketing played a key role in the war. Despite the important role of marketing in the war, scholars have not fully explored the impact of promotion and propaganda on the war effort. Marketing efforts related to World War I may not have had the impact as the millions of soldiers who took active part in the conflict. It did however play an important role in the filling the ranks of the armed forces of the nations involved in the war. The United States, and other nations, during the war developed increasingly more sophisticated marketing, public relations, and propaganda campaigns in order to culturally mobilize the home front, and to recruit additional fresh troops into the conflict in those nations which did not engage in conscription. Efforts to market World War I helped develop marketing tactics used today to brand and publicize products and services. A key figure in the efforts to market World War I to the American public was George Creel. Creel was the head of the Committee on Public Information. Creel was a successful promoter and champion of many of the reform causes of the early part of the 20th Century. Creel’s marketing efforts impact on America’s role in World War I was indeed significant. The topic of this paper is the impact of World War I on the development of marketing related tools and techniques. Many of the techniques and tools developed during the Great War are still used today to sell products, in the United States and in the rest of the world presently. Much of this paper will focus on the efforts of George Creel and the Committee on Public Information efforts to promote World War I, and how this organization impacted the development of marketing during its short existence.

Silk Fibroin Materials: Biomedical Applications and Perspectives.

The golden rule in tissue engineering is the creation of a synthetic device that simulates the native tissue, thus leading to the proper restoration of its anatomical and functional integrity, avoiding the limitations related to approaches based on autografts and allografts. The emergence of synthetic biocompatible materials has led to the production of innovative scaffolds that, if combined with cells and/or bioactive molecules, can improve tissue regeneration. In the last decade, silk fibroin (SF) has gained attention as a promising biomaterial in regenerative medicine due to its enhanced bio/cytocompatibility, chemical stability, and mechanical properties. Moreover, the possibility to produce advanced medical tools such as films, fibers, hydrogels, 3D porous scaffolds, non-woven scaffolds, particles or composite materials from a raw aqueous solution emphasizes the versatility of SF. Such devices are capable of meeting the most diverse tissue needs; hence, they represent an innovative clinical solution for the treatment of bone/cartilage, the cardiovascular system, neural, skin, and pancreatic tissue regeneration, as well as for many other biomedical applications. The present narrative review encompasses topics such as (i) the most interesting features of SF-based biomaterials, bare SF's biological nature and structural features, and comprehending the related chemo-physical properties and techniques used to produce the desired formulations of SF; (ii) the different applications of SF-based biomaterials and their related composite structures, discussing their biocompatibility and effectiveness in the medical field. Particularly, applications in regenerative medicine are also analyzed herein to highlight the different therapeutic strategies applied to various body sectors.

Advanced 3D In Vitro Lung Fibrosis Models: Contemporary Status, Clinical Uptake, and Prospective Outlooks.

Fibrosis has been characterized as a global health problem and ranks as one of the primary causes of organ dysfunction. Currently, there is no cure for pulmonary fibrosis, and limited therapeutic options are available due to an inadequate understanding of the disease pathogenesis. The absence of advanced in vitro models replicating dynamic temporal changes observed in the tissue with the progression of the disease is a significant impediment in the development of novel antifibrotic treatments, which has motivated research on tissue-mimetic three-dimensional (3D) models. In this review, we summarize emerging trends in preparing advanced lung models to recapitulate biochemical and biomechanical processes associated with lung fibrogenesis. We begin by describing the importance of in vivo studies and highlighting the often poor correlation between preclinical research and clinical outcomes and the limitations of conventional cell culture in accurately simulating the 3D tissue microenvironment. Rapid advancement in biomaterials, biofabrication, biomicrofluidics, and related bioengineering techniques are enabling the preparation of in vitro models to reproduce the epithelium structure and operate as reliable drug screening strategies for precise prediction. Improving and understanding these model systems is necessary to find the cross-talks between growing cells and the stage at which myofibroblasts differentiate. These advanced models allow us to utilize the knowledge and identify, characterize, and hand pick medicines beneficial to the human community. The challenges of the current approaches, along with the opportunities for further research with potential for translation in this field, are presented toward developing novel treatments for pulmonary fibrosis.

Advances in Mask-Modulated Lensless Imaging

Lensless imaging allows for designing imaging systems that are free from the constraints of traditional imaging architectures. As a broadly investigated technique, mask-modulated lensless imaging encodes light signals via a mask plate integrated with the image sensor, which is more compacted, with scalability and compressive imaging abilities. Here, we review the latest advancements in mask-modulated lensless imaging, lensless image reconstruction algorithms, related techniques, and future directions and applications.

Enantioseparation of organometallic compounds by electromigration techniques.

Over time, chiral organometallic compounds have attracted great interest in several fields, with applications going across several disciplines of chemical, biological, medical, and material sciences. In the last decades, due to advancements in molecular design and computational modeling, the chemistry of chiral transition metal complexes had a remarkable flowering, with the development of new structures for applications in asymmetric synthesis, bioinorganic chemistry, and molecular recognition. In these fields, fast chiral analysis to determine the enantiomeric purity of organometallic structures prepared by asymmetric synthesis, and for high-throughput screening of analytes, catalysts, and reactions, is very important. Capillary electrophoresis and related techniques proved to be extremely versatile for chiral analysis, showing unsurpassed advantages compared to chromatography like low consumption of materials, production of limited amounts of waste, fast equilibration, and possibility to replace easily type and concentration of the chiral selector, among others. Furthermore, electromigration techniques may be useful to gain details about the stereochemistry of the enantiomers of new compounds and to study analyte-selector noncovalent interactions at molecular level. On this basis, this short review aims to provide the reader with a comprehensive view on the enantioseparation of organometallic compounds by electromigration techniques, examining the topic from the historical perspective and showing what was made in this field so far, an essential know-how for developing new and advanced applications in the next future.

Positum MGS Coaching Protocol in the Framework of Positive Group Psychotherapy Training for Specialists Working with Groups of Children and Adults Affected by War, Disasters and Refugee Crises: a Case Study

War brings destruction in the broader sense, forcing millions to flee their homes, and resulting in an increased burden of depression, anxiety, PTSD and other mental and behavioral disorders. This burden grows even more in the framework of pandemics and natural disasters, as social and protection services experience crisis of resources. Providing one-to-one mental health care is no longer feasible for masses of refugee and internally displaced children and adults who need support, and, as a result, the need to have increasing numbers of mental health specialists who intervene in groups becomes more and more emergent. The paper reviews the Positum MGS approach to work with groups of children and adults in refugee settings, and not only, suggests the ‘Positum MGS coaching protocol’ within the framework of group Positive Psychotherapy (PPT after Peseschkian, since 1977)TM. Empirically supported and demonstrated by a case in the paper, the protocol being applied by a Positum MGS trainer or team of trainers, during an in-person training of 4-5 days, helps trainees greatly in learning to use psychosocial transcultural games and related techniques as resilience-building tools with groups of children, adolescents or adults. Either in the position of PPT trainers, psychotherapists, consultants or students, the participants in a Positum MGS core training and coaching or PPT training at basic or master levels with a focus of working with groups, can be supported through the suggested coaching protocol and questions in line with the 5-stage consultation of PPT.

Micro- and Nanostructured Fibrous Composites via Electro-Fluid Dynamics: Design and Applications for Brain.

The brain consists of an interconnected network of neurons tightly packed in the extracellular matrix (ECM) to form complex and heterogeneous composite tissue. According to recent biomimicry approaches that consider biological features as active components of biomaterials, designing a highly reproducible microenvironment for brain cells can represent a key tool for tissue repair and regeneration. Indeed, this is crucial to support cell growth, mitigate inflammation phenomena and provide adequate structural properties needed to support the damaged tissue, corroborating the activity of the vascular network and ultimately the functionality of neurons. In this context, electro-fluid dynamic techniques (EFDTs), i.e., electrospinning, electrospraying and related techniques, offer the opportunity to engineer a wide variety of composite substrates by integrating fibers, particles, and hydrogels at different scales-from several hundred microns down to tens of nanometers-for the generation of countless patterns of physical and biochemical cues suitable for influencing the in vitro response of coexistent brain cell populations mediated by the surrounding microenvironment. In this review, an overview of the different technological approaches-based on EFDTs-for engineering fibrous and/or particle-loaded composite substrates will be proposed. The second section of this review will primarily focus on describing current and future approaches to the use of composites for brain applications, ranging from therapeutic to diagnostic/theranostic use and from repair to regeneration, with the ultimate goal of providing insightful information to guide future research efforts toward the development of more efficient and reliable solutions.

Understanding the Pathophysiology of Mental Diseases and Early Diagnosis Thanks to Electrophysiological Tools: Some Insights and Empirical Facts.

Objective. Neurophysiological tools remain indispensable instruments in the assessment of psychiatric disorders. These techniques are widely available, inexpensive and well tolerated, providing access to the assessment of brain functional alterations. In the clinical psychiatric context, electrophysiological techniques are required to provide important information on brain function. While there is an immediate benefit in the clinical application of these techniques in the daily routine (emergency assessments, exclusion of organic brain alterations), these tools are also useful in monitoring the progress of psychiatric disorders or the effects of therapy. There is increasing evidence and convincing literature to confirm that electroencephalography and related techniques can contribute to the diagnostic workup, to the identification of subgroups of disease categories, to the assessment of long-term causes and to facilitate response predictions. Methods and Results. In this report we focus on 3 different novel developments of the use of neurophysiological techniques in 3 highly prevalent psychiatric disorders: (1) the value of EEG recordings and machine learning analyses (deep learning) in order to improve the diagnosis of dementia subtypes; (2) the use of mismatch negativity in the early diagnosis of schizophrenia; and (3) the monitoring of addiction and the prevention of relapse using cognitive event-related potentials. Empirical evidence was presented. Conclusion. Such information emphasized the important role of neurophysiological tools in the identification of useful biological markers leading to a more efficient care management. The potential of the implementation of machine learning approaches together with the conduction of large cross-sectional and longitudinal studies was also discussed.

Methodology and Experimental Protocol for Studying Learning and Motor Control in Neuromuscular Structures in Pilates.

The benefits of Pilates have been extensively researched for their impact on muscular, psychological, and cardiac health, as well as body composition, among other aspects. This study aims to investigate the influence of the Pilates method on the learning process, motor control, and neuromuscular trunk stabilization, specifically in both experienced and inexperienced practitioners. This semi-randomized controlled trial compares the level of experience among 36 Pilates practitioners in terms of motor control and learning of two Pilates-based skills: standing plank and side crisscross. Data will be collected using various assessment methods, including abdominal wall muscle ultrasound (AWMUS), shear wave elastography (SWE), gaze behavior (GA) assessment, electroencephalography (EEG), and video motion. Significant intra- and inter-individual variations are expected, due to the diverse morphological and psychomotor profiles in the sample. The adoption of both linear and non-linear analyses will provide a comprehensive evaluation of how neuromuscular structures evolve over time and space, offering both quantitative and qualitative insights. Non-linear analysis is expected to reveal higher entropy in the expert group compared to non-experts, signifying greater complexity in their motor control. In terms of stability, experts are likely to exhibit higher Lyapunov exponent values, indicating enhanced stability and coordination, along with lower Hurst exponent values. In elastography, experienced practitioners are expected to display higher transversus abdominis (TrA) muscle elasticity, due to their proficiency. Concerning GA, non-experts are expected to demonstrate more saccades, focus on more Areas of Interest (AOIs), and shorter fixation times, as experts are presumed to have more efficient gaze control. In EEG, we anticipate higher theta wave values in the non-expert group compared to the expert group. These expectations draw from similar studies in elastography and correlated research in eye tracking and EEG. They are consistent with the principles of the Pilates Method and other scientific knowledge in related techniques.

From Waste to Value: Recent Insights into Producing Vanillin from Lignin.

Vanillin, one of the most widely used and appreciated flavoring agents worldwide, is the main constituent of vanilla bean extract, obtained from the seed pods of various members belonging to the Orchidaceae family. Due to the great demand in the food confectionery industry, as well as in the perfume industry, medicine, and more, the majority of vanillin used today is produced synthetically, and only less than one percent of the world's vanilla flavoring market comes directly from the traditional natural sources. The increasing global demand for vanillin requires alternative and overall sustainable new production methods, and the recovery from biobased polymers, like lignin, is an environmentally friendly alternative to chemical synthesis. The present review provides firstly an overview of the different types of vanillin, followed by a description of the main differences between natural and synthetic vanillin, their preparation, the market of interest, and the authentication issues and the related analytical techniques. Then, the review explores the real potentialities of lignin for vanillin production, presenting firstly the well-assessed classical methods and moving towards the most recent promising approaches through chemical, biotechnological and photocatalytic methodologies, together with the challenges and the principal issues associated with each technique.

Microbial colony sequencing combined with metabolomics revealed the effects of chronic hexavalent chromium and nickel combined exposure on intestinal inflammation in mice

The pollution and toxic effects of hexavalent chromium [Cr(VI)] and divalent nickel [Ni(II)] have become worldwide public health issues. However, the potential detailed effects of chronic combined Cr(VI) and Ni exposure on colonic inflammation in mice have not been reported. In this study, 16S rDNA sequencing, metabolomics data analysis, qPCR and other related experimental techniques were used to comprehensively explore the mechanism of toxic damage and the inflammatory response of the colon in mice under the co-toxicity of chronic hexavalent chromium and nickel. The results showed that long-term exposure to Cr(VI) and/or Ni resulted in an imbalance of trace elements in the colon of mice with significant inflammatory infiltration of tissues. Moreover, Cr(VI) and/or Ni poisoning upregulated the expression levels of IL-6, IL-18, IL-1β, TNF-α, IFN-γ, JAK2 and STAT3 mRNA, and downregulated IL-10 mRNA, which was highly consistent with the trend in protein expression. Combined with multiomics analysis, Cr(VI) and/or Ni could change the α diversity and β diversity of the gut microbiota and induce significant differential changes in metabolites such as Pyroglu-Glu-Lys, Val-Asp-Arg, stearidonic acid, and 20-hydroxyarachidonic acid. They are also associated with disorders of important metabolic pathways such as lipid metabolism and amino acid metabolism. Correlation analysis revealed that there was a significant correlation between gut microbes and metabolites (P < 0.05). In summary, based on the advantages of comprehensive analysis of high-throughput sequencing sets, these results suggest that chronic exposure to Cr(VI) and Ni in combination can cause microbial flora imbalances, induce metabolic disorders, and subsequently cause colonic damage in mice. These data provide new insights into the toxicology and molecular mechanisms of Cr(VI) and Ni.

Linearized waveform inversion for vertical transversely isotropic elastic media: Methodology and multi-parameter crosstalk analysis

Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs. Seismic migration aims at obtaining structural images of subsurface geologic discontinuities. More specifically, seismic migration estimates the reflectivity function (stacked average reflectivity or pre-stack angle-dependent reflectivity) from seismic reflection data. On the other hand, seismic inversion quantitatively estimates the intrinsic rock properties of subsurface formulations. Such seismic inversion methods are applicable to detect hydrocarbon reservoirs that may exhibit lateral variations in the inverted parameters. Although there exist many differences, pre-stack seismic migration is similar with the first iteration of the general linearized seismic inversion.Usually, seismic migration and inversion techniques assume an acoustic or isotropic elastic medium. Unconventional reservoirs such as shale and tight sand formation have notable anisotropic property. We present a linearized waveform inversion (LWI) scheme for weakly anisotropic elastic media with vertical transversely isotropic (VTI) symmetry. It is based on two-way anisotropic elastic wave equation and simultaneously inverts for the localized perturbations (ΔVp0/Vp0,ΔVs0/Vs0,Δϵ,Δδ) from the long-wavelength reference model. Our proposed VTI-elastic LWI is an iterative method that requires a forward and an adjoint operator acting on vectors in each iteration. We derive the forward Born approximation operator by perturbation theory and adjoint operator via adjoint-state method. The inversion has improved the quality of the images and reduces the multi-parameter crosstalk comparing with the adjoint-based images. We have observed that the multi-parameter crosstalk problem is more prominent in the inversion images for Thomsen anisotropy parameters. Especially, the Thomsen parameter δ is the most difficult to resolve. We also analyze the multi-parameter crosstalk using scattering radiation patterns.The linearized waveform inversion for VTI-elastic media presented in this article provides quantitative information of the rock properties that has the potential to help identify hydrocarbon reservoirs.

Exploring the spatial attributes of streets in Lu Xun’s hometown of Shaoxing, China, through image semantic segmentation

Image semantic segmentation, a deep learning algorithm, enables the recognition of pixel collections that form distinct categories, allowing for the identification of vehicles, pedestrians, traffic signs, pavement, and other road features. In urban and architectural design domains, image semantic segmentation and related techniques empower practitioners and researchers to efficiently analyze the distribution of public spaces. This application facilitates a better understanding of how people interact with urban environments, ultimately improving the design of functional and inviting spaces. This paper presents an analysis of images of different streets within the Lu Xun Heritage Area in Shaoxing, Zhejiang Province, China, which were obtained through onsite photography. The images were sampled, segmented, and compared to assess the spatial characteristics of distinct street types. A self-trained semantic segmentation model based on the Cityscapes dataset and the PaddlePaddle framework was employed to statistically analyze space variations across various dimensions. This analysis contributes to a better understanding of historical street structure and provides insights into the integration of artificial intelligence in urban planning and design.