Three patients with multi-year histories of relapsing and remitting Lyme disease and associated co-infections despite extended antibiotic therapy were each given double dose dapsone combination therapy (DDD CT) for a total of 7-8 weeks. At the completion of therapy, all three patients major Lyme symptoms remained in remission for a period of 25-30 months. In conclusion, Double dose dapsone therapy could represent a novel and effective anti-infective strategy in chronic Lyme disease/PTLDS, especially in those individuals who have failed regular dose dapsone combination therapy (DDS CT) or standard antibiotic protocols. A randomized, blinded, placebo-controlled trial is warranted to evaluate the efficacy of DDD CT in those individuals with chronic Lyme disease/PTLDS.

COVID-19 has created anxiety not only in individuals but also in health organizations, and countries worldwide. Not a single industry is left un-influenced and loss is being estimated in billions of dollars. The widespread of this pandemic disease has challenged researchers all over the world. Some of the researchers are working to invent its cure while, others are applying computing technologies to stop its spread, by analyzing and identifying patterns for prediction and forecasting. This is by no doubt the hottest area of research for the last 100 years. This survey has targeted the research published in computing sub-domains to combat the pandemic. The survey has clustered the scientific efforts into logical groups: surveillance, metrological effects, social media analytics, image processing and business and economy, analysis and modeling. It will serve as a leading source for the followings: researchers who want to identify what has been achieved in different computing sub-domains, those who need fresh authenticated datasets openly accessible for different research contexts and what are future directions in this area of research. The findings of analysis and modeling can be also useful for government agencies who want to set priorities and formulate policies.

Human Perrault syndrome (PRLTS) is defined by autosomal recessive inheritance with primary ovarian insufficiency and early hearing loss. Most PRLTS disease proteins modulate mitochondrial transcription or translation. Among the genetic causes are ClpP mutations, which trigger also complete azoospermia, whose cellular and molecular underpinnings are unknown. Here, the ClpP-null mouse model was studied by global transcriptomics, proteomics, RT-qPCR, immunoblots, tissue fractionation, testis histology, and was crossed with STING/IFNAR mutants. Spermatogenesis showed accumulated early spermatocytes, versus deficits of desynapsis and kinetochore factors; excess Dazl/Stra8 and acetylSMC3, versus deficient SHCBP1L, were molecular correlates. Spermiogenesis showed few round spermatids, tsHMG/TFAM in elongated spermatids was absent; transcripts for tail/acrosome factors were downregulated from start. Nuclear anomalies included a failed Rec8 induction, early BRDT deficiency, histone H3 cleavage, and cGAMP increase, among antiviral responses typical of ClpP-mutants. However, deletion of downstream innate immune signals STING/IFNAR failed to reestablish fertility. As mitochondrial triggers, we observed accumulation of ClpX, with PTCD1, POLDIP2, GRSF1, ALKBH7, DNAJA3, AURKAIP1, VWA8, and Perrault proteins ERAL1, PEO1, HARS2, partially showing nuclear redistribution. ClpP-depletion is known to cause extra-mitochondrial release of mispacked mtDNA/mtRNA/protein complexes. Now we define nuclear inflammatory responses and meiotic arrest as consequences, similar to observations in mito-mice and mutator-mice.

Human Perrault syndrome (PRLTS) is defined by autosomal recessive inheritance with primary ovarian insufficiency and early hearing loss. Most PRLTS disease proteins modulate mitochondrial transcription or translation. Among the genetic causes are ClpP mutations, which trigger also complete azoospermia, whose cellular and molecular underpinnings are unknown. Here, the ClpP-null mouse model was studied by global transcriptomics, proteomics, RT-qPCR, immunoblots, tissue fractionation, testis histology, and was crossed with STING/IFNAR mutants. Spermatogenesis showed accumulated early spermatocytes, versus deficits of desynapsis and kinetochore factors; excess Dazl/Stra8 and acetylSMC3, versus deficient SHCBP1L, were molecular correlates. Spermiogenesis showed few round spermatids, tsHMG/TFAM in elongated spermatids was absent; transcripts for tail/acrosome factors were downregulated from start. Nuclear anomalies included a failed Rec8 induction, early BRDT deficiency, histone H3 cleavage, and cGAMP increase, among antiviral responses typical of ClpP-mutants. However, deletion of downstream innate immune signals STING/IFNAR failed to reestablish fertility. As mitochondrial triggers, we observed accumulation of ClpX, with PTCD1, POLDIP2, GRSF1, ALKBH7, DNAJA3, AURKAIP1, VWA8, and Perrault proteins ERAL1, PEO1, HARS2, partially showing nuclear redistribution. ClpP-depletion is known to cause extra-mitochondrial release of mispacked mtDNA/mtRNA/protein complexes. Now we define nuclear inflammatory responses and meiotic arrest as consequences, similar to observations in mito-mice and mutator-mice.

In humans, age-associated degrading changes are observed in molecular and cellular processes underly the time-dependent decline in spatial navigation, time perception, cognitive and psy-chological abilities, and memory. Cross talk of biological, cognitive, and psychological clocks provides an integrative contribution to healthy and advanced aging. At the molecular level, ge-nome, proteome, and lipidome instability are widely recognized as the primary causal factors in aging. We narrow attention to the roles of protein aging linked to prevalent amino acids chirali-ty, enzymatic and spontaneous (non-enzymatic) post-translational modifications (PTMs SP), and non-equilibrium phase transitions. The homochirality of protein synthesis, resulting in the steady-state non-equilibrium condition of protein structure, makes them prone to multiple types of enzymatic and spontaneous PTMs, including racemization and isomerization. Spontaneous racemization leads to the loss of the balanced prevalent chirality. Advanced biological aging re-lated to irreversible PTMs SP has been associated with the nontrivial interplay between poor so-matic and mental health conditions. Through stress response systems (SRS), the environmental and psychological stressors contribute to the age-associated “collapse” of protein homochirality. The role of prevalent protein chirality and entropy of protein folding in biological aging is mainly overlooked. In a more generalized context, the time-dependent shift from enzymatic to the non-enzymatic transformation of biochirality might represent an important and yet un-der-appreciated hallmark of aging.

The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use-cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times of its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today's living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset, by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 tweets about exoskeletons that were posted in a 5-year period from May 21, 2017, to May 21, 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

Since the outbreak of COVID-19, social media platforms, such as Twitter, have experienced a tremendous increase in conversations related to Long COVID. The term “Long COVID” describes the persistence of symptoms of COVID-19 for several weeks or even months following the initial infection. Recent works in this field have focused on sentiment analysis of Tweets related to COVID-19 to unveil the multifaceted spectrum of emotions, viewpoints, and perspectives held by the Twitter community. However, most of these works did not focus on Long COVID, and the few works that focused on Long COVID have limitations. Furthermore, no prior work in this field has investigated Tweets where individuals self-reported experiencing Long COVID on Twitter. The work presented in this paper aims to address these research challenges by presenting multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between May 25, 2020, and January 31, 2023. First, the analysis shows that the average number of Tweets per month where individuals self-reported Long COVID on Twitter, has been considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings of sentiment analysis using VADER show that the percentage of Tweets with positive, negative, and neutral sentiment were 43.12%, 42.65%, and 14.22%, respectively. Third, the analysis of sentiments associated with these Tweets also shows that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

In this study, an optimized multi-material system was designed and developed to print samples in various applications, including biomedical fields (e.g., mandibular bone loss). To improve the mechanical and biological properties of scaffolds utilized for dental bone loss applications, a multi-material setup was devised, which employs digital light processing technology. This setup consists of a linear system comprising two resin vats and one ultrasonic cleaning tank, enabling the integration of diverse materials and structures to optimize the composition of the scaffold. This approach was used to print multi-material PLLA scaffolds containing 20 wt.%. HA on the interior side, and PLLA containing 1 wt.% GO on the exterior surface of the scaffold, which were evaluated mechanically and biologically after printing. The scaffold was designed using a triply periodic minimal surface (TPMS) lattice structure, which is known to possess favorable mechanical and biological properties. Various multi-material samples were successfully printed and evaluated to illustrate the multiple-material setup's potential for ensuring proper function, cleaning, and adequate interface bonding. By numerically evaluating several TMPS structures, a novel Gyroid TPMS scaffold with a nominal porosity of 50% was developed and validated experimentally. The biological properties of the scaffolds were also evaluated, including surface morphology, (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) MTT assay, and cell adhesion. Based on the results, multi-material components with the least contaminations with suitable mechanical and biological properties were successfully printed. By combining PLLA-HA and PLLA-GO, this innovative technique holds tremendous potential for enhancing the effectiveness of regenerative procedures in the field of dentistry.

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking in account several concepts, including data acquisition, data processing, data fusion, pattern recognition, and machine learning. These concepts can be mapped in a module of the framework, including the use and creation of several algorithms. For the development of a framework that works in several conditions, e.g., without Internet connection, these algorithms should take in account the hardware and software limitations of the mobile devices to run all main tasks locally. The main purpose of this paper is related to the presentation the sensors, algorithms, and architecture of the proposed approach.

Visualising textual content could be helpful to professionals as well as amateurs across several fields. However, training a text-to-image generator in the mainstream domain requires large amounts of paired text-image and data, which is too expensive to collect since labeling millions of images and videos can be tiresome. GANs like StackGAN and StyleGAN can be considered as solutions to generate images from text. But the images generated may be of low accuracy and resolution, and the entire processing can be highly time-consuming. Moreover, image generation is a notion that is still being researched. Hence, the process of developing a Video Generation model necessitates substantial research. Despite the need for such a model, modern technology has lagged behind the solutions to this problem. This proposal suggests combining two methods, Text modification for Action Definition (TexAD) and SeQuential Image Generation for Video Synthesis (SQIGen). The proposed solution synthesises a sequence of images from textual information feeds and combines these images to create a video. TexAD uses Natural Language Processing and Deep Learning techniques to process, classify and modify text data. SQIGen is an extension of the VQGAN+CLIP neural network architecture that generates a sequence of images from the modified text data

A topic of growing interest in urban remote sensing is the automated extraction of geometrical building information for 3D city modeling. Roof geometry information is useful for applications such as urban planning, solar potential estimation and telecommunication installation planning, and wind flow simulations for pollutant diffusion analysis. Recent research has proven that the advance in remote sensing technologies and deep learning methods offer the prospects of deriving the roof structure information accurately and efficiently. In this study, we propose a Vectorized Roof Extractor- method based on Fully Convolutional Networks (FCNs) and advanced polygonization method to extract roof structure from aerial imagery and a normalized Digital Surface Models (nDSM) in a regularized vector format. The roof structure consists of building outlines, external edges of the building roof, inner rooflines, internal intersections of the main roof planes. The methodology is comprised of segmentation, vectorization and post-processing for outer rooflines, external edges of the building roof, and inner rooflines, and internal intersections of the main roof planes. For the comparison, we adapt the Frame field Learning (FFL) method originally designed to extract building polygons [1]. Our experiments are conducted on a custom data set derived for the city of Enschede, The Netherlands, using aerial imagery, nDSM and manually digitized training polygons. The results show that the proposed Vectorized Roof Extractor outperformed adapted FFL on PoLiS distance with values of 3.5 m and 1.2 m for outlines and inner rooflines, respectively. Furthermore, the model surpassed the adapted FFL on PoLiS-thresholded F-score for outlines and inner rooflines, with 0.31 and 0.57, respectively. The Vectorized Roof Extractor produced adequate visual results, with straighter walls and fewer missed inner roofline detections. It can predict buildings with common walls thanks to skeleton graph computation. To summarize, the proposed method is suitable for urban applications and has the potential to be improved further.

Emotion recognition and social inference impairments are well-documented post-traumatic brain injury (TBI) yet the mechanisms underpinning these are not fully understood. We examined dynamic emotion recognition, social inference abilities, and eye fixation patterns between adults with and without TBI. Eighteen individuals with TBI and 18 matched non-TBI participants were recruited and underwent all three components of The Assessment of Social Inference Test (TASIT). The TBI group were less accurate in identifying emotions compared to the non-TBI group. Individuals with TBI also scored lower when distinguishing sincere and sarcastic conversations but scored similarly to those without TBI during lie vignettes. Finally, those with TBI also had difficulty understanding the actor’s intentions, feelings, and beliefs compared to participants without TBI. No group differences were found for eye fixation patterns and there were no associations between fixations and behavioural accuracy scores. This conflicts with previous studies and might be related to an important distinction between static and dynamic stimuli. Visual strategies appeared goal- and stimulus-driven, with attention being distributed to the most diagnostic area of the face for each emotion. These findings suggest that low-level visual deficits may not be modulating emotion recognition and social inference disturbances post-TBI.

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.

Usually, in ground-penetrating radar (GPR) datasets the user defines the limits between the useful signal and the noise through standard filtering to isolate the effective signal as much as possible. However, there are true reflections that mask the coherent reflectors that can be considered noise. In archaeological sites these clutter reflections are caused by scattering with origin in subsurface elements (e.g., isolated masonry, ceramic objects and archaeological collapses). Its elimination is difficult because the wavelet parameters similar to coherent reflections and there is a risk of creating artifacts. In this study a procedure to filtering the clutter reflection noise (CRN) from GPR datasets is presented. The CRN filter is a singular value decomposition-based method (SVD), applied in the 2D spectral domain. This CRN filtering was tested in a dataset obtained from a controlled laboratory environment, to establish a mathematical control of this algorithm. Also, it has been applied in a 3D-GPR dataset acquired in the Roman villa of Horta da Torre (Fronteira, Portugal), which is an uncontrolled environment. The results show an increase in the quality of archaeological-GPR planimetry that were verified via archaeological excavation.

Many aging adults experience some form of hearing problems that may arise from auditory peripheral damage. However, it has been increasingly acknowledged that hearing loss is not only a dysfunction of the auditory periphery but results from changes within the entire auditory system, from periphery to cortex. Damage to the auditory periphery is associated with an increase in neural activity at various stages throughout the auditory pathway. Here, we review neurophysiological evidence of hyperactivity, auditory perceptual difficulties that may result from hyperactivity, and outline open conceptual and methodological questions related to the study of hyperactivity. We suggest that hyperactivity alters all aspects of hearing – including spectral, temporal, spatial hearing – and, in turn, impairs speech comprehension when background sound is present. By focusing on the perceptual consequences of hyperactivity and the potential challenges of investigating hyperactivity in humans, we hope to bring animal and human electrophysiologists closer together to better understand hearing problems in older adulthood.

Bello's stochastic linear time-varying system theory has been widely used in the wireless communications literature to characterize multipath fading channel statistics. In the context of radar backscatter, this formulation allows for statistical characterization of distributed radar targets in range and Doppler using wide-sense stationary uncorrelated scattering (WSSUS) models. WSSUS models separate the channel from the effect of the waveform and receive filter, making it an ideal formulation for waveform design problems. Of particular interest in the radar waveform design community is the ability to suppress unwanted backscatter from the earth's surface, known as clutter. Various methods for estimating WSSUS system functions have been studied in the literature, but to date, no analytic expressions for radar surface clutter range-Doppler scattering functions exist. In this work we derive a wideband generalization of the Jakes Doppler spectrum model, which is widely used in the wireless communications literature, adapt it for use in radar problems, and show how the maximum entropy method can be used to extend this model to account for internal clutter motion. Validation of the spectral and stationarity properties of the proposed model against a subset of the Australian Ingara sea clutter database is performed, and good agreement is shown.

This paper proposes a novel approach, based on the adaptive rate processing and analysis, for the isolated speech recognition. The idea is to smartly combine the event-driven signal acquisition and windowing along with adaptive rate processing, analysis and classification for realizing an effective isolated speech recognition. The incoming speech signal is digitized with an event-driven A/D converter (EDADC). The output of EDADC is windowed with an activity selection process. These windows are later on resampled uniformly with an adaptive rate interpolator. The resampled windows are de-noised with an adaptive rate filter and their spectrum are computed with an adaptive resolution short time Fourier transform (ARSTFT). Later on, the magnitude, Delta and Delta-Delta spectral coefficients are extracted. The Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The system functionality is tested for a case study and results are presented. An 8.2 times reduction in acquired number of samples is achieved by the devised approach as compared to the classical one. It aptitudes a significant computational gain and power consumption reduction of the proposed system over the counter classical ones. An average subject dependent isolated speech recognition accuracy of 96.8% is achieved. It shows that the proposed approach is a potential candidate for the automatic speech recognition applications like rehabilitation centers, smart call centers, smart homes, etc.

In recent years, weeds is responsible for most of the agricultural yield losses. To deal with this threat, farmers resort to spraying pesticides throughout the field. Such method not only requires huge quantities of herbicides but impact environment and humans health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide at the right place and at the right time (Precision Agriculture). Nowadays, Unmanned Aerial Vehicle (UAV) is becoming an interesting acquisition system for weeds localization and management due to its ability to obtain the images of the entire agricultural field with a very high spatial resolution and at low cost. Despite the important advances in UAV acquisition systems, automatic weeds detection remains a challenging problem because of its strong similarity with the crops. Recently Deep Learning approach has shown impressive results in different complex classification problem. However, this approach needs a certain amount of training data but, creating large agricultural datasets with pixel-level annotations by expert is an extremely time consuming task. In this paper, we propose a novel fully automatic learning method using Convolutional Neuronal Networks (CNNs) with unsupervised training dataset collection for weeds detection from UAV images. The proposed method consists in three main phases. First we automatically detect the crop lines and using them to identify the interline weeds. In the second phase, interline weeds are used to constitute the training dataset. Finally, we performed CNNs on this dataset to build a model able to detect the crop and weeds in the images. The results obtained are comparable to the traditional supervised training data labeling. The accuracy gaps are 1.5% in the spinach field and 6% in the bean field.

Exposure to adverse childhood experiences (ACE) has been found to have a profound negative impact on multiple child outcomes, including academic achievement, social cognition patterns, and behavioral adjustment. However, these links have yet to be examined in preschool children that are already experiencing behavior or social-emotional problems. Thus, the present study examined the links between the caregiver's and the child's exposure to ACE and multiple child and caregiver's outcomes in a sample of 30 preschool children enrolled in a Therapeutic Nursery Program (TNP). Children are typically referred to this TNP due to significant delays in their social emotional development that often result in difficulty functioning in typical childcare, home, and community settings. Analyses revealed some contradictory patterns that may be specific to this clinical sample. Children with higher exposure to ACE showed more biased social information processing patterns and their caregivers reported lower child social skills than caregivers of children with less exposure, however their inhibitory control levels were higher (better control) and staff reported that these children exhibited better social skills as well as better approaches to learning than children with less exposure. No such contradictions were found in relation to the caregiver's exposure to ACE, as it was positively associated with a number of negative child and caregiver outcomes.

: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for speech disabled individuals, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems were found to hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.

This article provides a general overview of the bio-inspired computing method called DNA-Based Computing (DBC), including its theory and applications. The main theme of DBC is the central dogma of molecular biology (once information of DNA/RNA has got into a protein, it can’t get out again), i.e., DNA to RNA (sequences of four types of nucleotides) and DNA/RNA to protein (sequence of twenty types of amino acids) are allowed, not the reverse ones. Thus, DBC transfers few-element information (DNA/RAN-like) to many-element information (protein-like), solving a given cognitive problem. DBC can take many forms; this article elucidates two main forms, denoted as DBC-1 and DBC-2. Using arbitrary numerical examples, it is shown that DBC-1 can solve the following cognitive problems: “similarity indexing between seemingly different but inherently identical objects” and “recognizing regions of an image separated by a complex boundary.” DBC-2 can solve the following cognitive problem: “pattern recognition when the relevant information is insufficient.” Smart manufacturing-based systems (digital twins and big data analytics) must solve the abovementioned problems to make the manufacturing enablers (machine tools and monitoring systems) more self-reliant and autonomous. Consequently, DBC can improve the cognitive problem-solving ability of smart manufacturing-relevant systems and, thereby, can enhance its biologicalization.

Humankind has entered a new era wherein a main characteristic is the convergence of various technologies providing services exerting major impact upon all aspects of human activity, be it social interactions as well as interactions with the natural environment. Fixed networks are about to play a major role in this convergence, since they form, along with mobile networks, the backbone that provides access to a broad gamut of services, accessible from any point of the globe. It is for this reason that we introduce a forward-looking approach for fixed networks, particularly focused on Fixed 6th Generation (F6G) networks. First, we adopt a novel classification scheme for the main F6G services, comprising six categories. This classification is based on the key service requirements, namely latency, capacity and connectivity. F6G networks differ from those of previous generations (F1G-F5G) in that they concurrently support multiple key requirements. We then propose concrete steps towards transforming the main elements of fixed networks, such as optical transceivers, optical switches etc. such that they satisfy the new F6G service requirements. Our study categorizes the main networking paradigm of optical switching into two categories namely ultra-fast and ultra-high capacity switching, tailored to different service categories. With regard to the transceiver physical layer, we propose: a) use of all-optical processing to mitigate performance barriers of analog to digital and digital to analog converters (ADC/DAC); b) exploitation of optical multi-band transmission, space division-multiplexing and adoption of more efficient modulation formats.

Neuromuscular symptoms in long COVID patients are common. Since adequate diagnostics are still missing, investigating muscle function might be beneficial. The holding capacity (maximal isometric Adaptive Force; AFisomax) was previously suggested to be especially vulnerable for impairments. This longitudinal, non-clinical study aimed to investigate the AF in long COVID patients in recovery process. AF parameters of elbow/hip flexors were assessed in 17 patients at three timepoints (pre: long COVID state, post: immediately after first treatment, end: recovery) by an objectified manual muscle test. The tester applied an increasing force on the limb of the patient, who had to resist isometrically for as long as possible. The intensity of 13 common symptoms were queried. At pre, patients started to lengthen their muscles at ~50% of the maximal AF (AFmax), which was then reached during eccentric motion, indicating unstable adaptation. At post and end, AFisomax increased significantly to ~99% and 100% of AFmax, respectively, reflecting stable adaptation. AFmax was statistically similar for all three timepoints. Symptoms intensity decreased significantly from pre to end. In conclusion, maximal holding capacity seems to be impaired in long COVID patients and increases with substantial health improvement. AFisomax might be a suitable sensitive functional parameter to assess long COVID patients and to support therapy process.

Fear extinction requires coordinated neural activity within the amygdala and medial prefrontal cortex (mPFC). Any behavior has a transcriptomic signature that is modified by environmental experiences, and specific genes are involved in functional plasticity and synaptic wiring during fear extinction. Here, we investigated the effects of optogenetic manipulations of prelimbic (PrL) pyramidal neurons on amygdala gene expression to analyze the specific transcriptional pathways involved in adaptive and maladaptive fear extinction. To this aim, transgenic mice were (or not) fear-conditioned and during the extinction phase they received optogenetic (or sham) stimulations over PrL pyramidal neurons. At the end of behavioral testing, electrophysiological (neural cellular excitability and Excitatory Post-Synaptic Currents) and morphological (spinogenesis) correlates were evaluated in the PrL pyramidal neurons. Furthermore, transcriptomic cell-specific RNA-analyses (differential gene expression profiling and functional enrichment analyses) were performed in amygdala pyramidal neurons. Our results show that the optogenetic activation of PrL pyramidal neurons in fear-conditioned mice induces fear extinction deficits, reflected in an increase of cellular excitability, excitatory neurotransmission, and spinogenesis of PrL pyramidal neurons, and in strong modifications of the transcriptome of amygdala pyramidal neurons. Understanding the electrophysiological, morphological and transcriptomic architecture of fear extinction may facilitate the comprehension of fear-related disorders.

Background: Infections of Coronavirus Disease-2019 (COVID-19) can cause long-term effects known as long COVID. This pilot study aimed to evaluate the feasibility of a clinical study as well as the efficacy and safety of traditional East Asian herbal medicines in alleviating fatigue and cognitive dysfunction “brain fog” in patients with long COVID. Methods: This prospective pilot study investigated the use of three types of herbal medicines, Bojungikki-tang (BIT), Kyungok-go (KOG), and Cheonwangbosim-dan (CBD), for a 12-week period as potential treatments for fatigue and cognitive dysfunction in patients with long COVID. Forty-five patients with long COVID were recruited, and one of three drugs was given based on the patient’s symptoms and pattern identification. The effect of herbal medications on fatigue and cognitive function outcomes was assessed over a 36-week period, with patient adherence closely monitored. Results: After 12 weeks of herbal drug administration, fatigue symptoms improved significantly across all groups, with treatment success rates of 80%, 53.33%, and 46.67% in the BIT, KOG, and CBD groups, respectively. However, “brain fog” symptoms showed less improvement, with treatment success rates of 40%, 46.67%, and 13.33% in the BIT, KOG, and CBD groups, respectively. All adverse events reported were mild and unrelated to the medication. The study design was found to be feasible with high medication adherence. Conclusions: This study demonstrated the feasibility of conducting a clinical trial with three herbal medicines to treat long COVID symptoms like fatigue and “brain fog.”

This study aims to put forth a new method to study the socio-spatial boundaries by using georeferenced community-authored reviews for restaurants. In this study, we show that food choice, drink choice, and restaurant ambience can be good indicators of socio-economic status of the ambient population in different neighborhoods. To this end, we use Yelp user reviews to distinguish different neighborhoods in terms of their food purchases and identify resultant boundaries in 10 North American metropolitan areas. This data-set includes restaurant reviews as well as a limited number of user check-ins and rating in those cities. We use Natural Language Processing (NLP) techniques to select a set of potential features pertaining to food, drink and ambience from Yelp user comments for each geolocated restaurant. We then select those features which determine one’s choice of restaurant and the rating that he/she provides for that restaurant. After identifying these features, we identify neighborhoods where similar taste is practiced. We show that neighborhoods identified through our method show statistically significant differences based on demographic factors such as income, racial composition, and education. We suggest that this method helps urban planners to understand the social dynamics of contemporary cities in absence of information on service-oriented cultural characteristics of urban communities.

This paper makes the case that in an Internet of Things (IoT) world where data processing has become pervasive, the assessment of whether or not the underlying (statistical) modeling assumptions are justified and appropriate should no longer be limited to the perspective of mathematical statistics alone. The paper argues that large parts of sound academic research in engineering lack practical merit in that, akin to a concept car, they are not market-ready, most commonly due to feasibility and liability issues. Through an analysis of both statistical and legal aspects it will be shown that the stoic pursuit of ‘optimality’ more often than not yields to risky and suboptimal outcomes when applied to actual physical world problems. To address this, the concept of ‘Defensive Signal Processing’ is introduced and future research directions are briefly outlined.

In this paper, we investigated transducer placement strategies for detecting crack in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The approach developed is for an expected damage location based on fracture mechanics, for example fatigue crack growth in a high stress location. To assess the performance of the developed approach, finite-element (FE) modelling of a damage tolerant aluminum fuselage has been performed by introducing an artificial crack at a rivet hole into the structural FE model and assessing its influence on the Lamb wave propagation, compared to a baseline measurement simulation. The efficient practical sensor position was determined from the largest change in area that is covered by reflected and missing wave scatter using an additive color model. Blob detection algorithms were employed in order to determine the boundaries of this area and to calculate the blob centroid. To demonstrate that the technique can be generalized, the results from different crack lengths and from tilted crack are also presented.

Arginine shows Jekyll and Hyde behavior in several respects. It participates in protein folding via ionic and H-bonds and cation-pi interactions; the charge and hydrophobicity of its side chain makes it a disorder-promoting amino acid. Its methylation in histones; RNA binding proteins; chaperones regulates several cellular processes. The arginine-centric modifications are important in oncogenesis and as biomarkers in several cardiovascular diseases. The cross-links involving arginine in collagen and cornea are involved in pathogenesis of tissues but have also been useful in tissue engineering and wound-dressing materials. Arginine is a part of active site of several enzymes such as GTPases, peroxidases, and sulfotransferases. Its metabolic importance is obvious as it is involved in production of urea, NO, ornithine and citrulline. It can form unusual functional structures such as molecular tweezers in vitro and sprockets which engage DNA chains as part of histones in vivo. It has been used in design of cell-penetrating peptides as drugs. Arginine has been used as an excipient in both solid and injectable drug formulations; its role in suppressing opalescence due to liquid-liquid phase separation is particularly very promising. It has been known as a suppressor of protein aggregation during protein refolding. It has proved its usefulness in protein bioseparation processes like ion-exchange, hydrophobic and affinity chromatographies. Arginine is an amino acid, whose importance in biological sciences and biotechnology continues to grow in diverse ways.

Humans have a unique cognitive ability known as relational abstraction, which allows us to identify logical rules and patterns beyond basic perceptual characteristics. This ability represents a key difference between how humans and other animals learn and interact with the world. With current large language models rivaling human intelligence in many regards, we investigated whether relational abstraction was an emergent chat completion capacity in various models. We find that despite their impressive language processing skills, all tested language models failed the relational match-to-sample (RMTS) test, a benchmark for assessing relational abstraction. These results challenge the assumption that advanced language skills inherently confer the capacity for complex relational reasoning. The paper highlights the need for a broader evaluation of AI cognitive abilities, emphasizing that language proficiency alone may not be indicative of certain higher-order cognitive processes thought to be supported by language.

In 1937,German mathematician L.Collatz proposed a conjeture:for any positive integer,if it is even,divide it by 2;if it is odd,multiply it by 3 and 1 to get an even number;keep going on and on, the final result is 1.This paper gives a sequence classification of odd numbers,and gives 1-1 mapping of odd numbers and odd number sequences,and uses these results to give a proof of this conjecture.

RNA polymerase II (RNAPII) frequently transcribes non-protein coding DNA sequences in eukaryotic genomes into long non-coding RNA (lncRNA). Here, we focus on the impact of the act of lncRNA transcription on nearby functional DNA units. Distinct molecular mechanisms linked to the position of lncRNA relative to the coding gene illustrate how non-coding transcription controls gene expression. We review the biological significance of the act of lncRNA transcription on DNA processing, highlighting common themes, such as mediating cellular responses to environmental changes. This review presents the background in chromatin signaling to appreciate examples in different organisms where we can interpret functions of non-coding DNA through the act of RNAPII transcription.

Nollywood, based on the idea of Bollywood from India, is a series of outstanding movies that originate from Nigeria. Unfortunately, while the movies are in English, they are hard to understand for many native speakers due to the dialect of English that is spoken. In this article, we accomplish two goals: (1) create a phonetic sub-title model that is able to translate Nigerian English speech to American English and (2) use the most advanced toxicity detectors to discover how toxic the speech is. Our aim is to highlight the text in these videos which is often times ignored for lack of dialectal understanding due the fact that many people in Nigeria speak a native language like Hausa at home.

The ability to control the temperature distribution T(t,r) and the rate of temperature change Rt,r inside glasses is important for their microstructuring. The lattice temperature is considered at time t, exceeding the electron-phonon thermalization time, and at a distance r from the center of the model spherical heating zone. In order to describe thermal excitations, the heat capacity of glasses must be considered as a function of time due to its long-term relaxation. A method for analytical calculation of T(t,r) and R(t,r) for glasses with dynamic heat capacity cdyn(t) is proposed. It is shown that during laser microstructuring the local cooling rate -R(t,r) significantly depends on the time dispersion of cdyn(t). It has been established that at the periphery of the model heating zone of the laser beam focus, the local cooling rate can reach more than 1011 K/s. Strong cooling rate gradients were found at the periphery of the heating zone, affecting the microstructure of the material. This effect is significantly enhanced by the time dispersion of cdyn(t). The effect associated with this time dispersion is significant even well above the glass transition temperature Tg, since even short relaxation times of the dynamic heat capacity cdyn(t) are significant.

The integration of Artificial Intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in the diagnosis and management of thoracic disorders. This study proposes a comprehensive framework, named Lung-DT, which leverages IoT sensors and AI algorithms to establish a digital representation of a patient’s respiratory health. Using the YOLOv8 neural network, the Lung-DT system accurately classifies chest X-Rays into five distinct categories of lung diseases, including "Normal," "Covid," "Lung Opacity," "Pneumonia," and "Tuberculosis". The system’s performance was evaluated on a chest X-Ray dataset, demonstrating an impressive average accuracy of 96.6% across all classes. Further tests (prediction) were conducted on the trained network using a third dataset available in the literature and completely unknown to the network, yielding an average accuracy of 98% across three classes. The proposed Lung-DT framework offers several advantages over conventional diagnostic methods. Firstly, it enables real-time monitoring of lung health through continuous data acquisition from IoT sensors, facilitating early diagnosis and intervention. Secondly, the AI-powered classification module provides automated and objective assessments of chest X-Rays, reducing dependence on subjective human interpretation. Thirdly, the twin digital representation of the patient’s respiratory health allows for comprehensive analysis and correlation of multiple data streams, providing valuable insights for personalized treatment plans. The integration of IoT sensors, AI algorithms, and DT technology within the Lung-DT system demonstrates a significant step towards improving thoracic healthcare. By enabling continuous monitoring, automated diagnosis, and comprehensive data analysis, the Lung-DT framework has enormous potential to enhance patient outcomes, reduce healthcare costs, and optimize resource allocation.

For Convolutional Neural Network (CNN), Depthwise Separable CNN (DSCNN) is a preferred architecture for Application Specific Integrated Circuit (ASIC) implementation on edge devices. It can benefit from a multi-mode approximate multiplier proposed in this work. The proposed approximate multiplier uses two 4-bit multiplication operations to implement a 12-bit multiplication operation by reusing the same multiplier array. With this approximate multiplier, sequential multiplication operations are pipelined in a modified DSCNN to fully utilize the PE array in the convolutional layer. This Approximate (A-DSCNN) was implemented on TSMC 40-nm CMOS process with a supply voltage of 0.9 V. At the clock frequency of 200 MHz, the design achieves 4.78 GOPs/mW while occupying 1.24 mm x 1.24 mm silicon area. Compared to conventional DSCNN implemented in a similar process node, the chip area and power consumption were reduced by 53% and 25%, while the throughput was improved by 17%.

The use of artificial intelligence in natural language processing (NLP) has significantly contributed to the advancement of natural language applications such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources such as webpages, blogs, emails, social media, and articles, one of the most common tasks in natural language processing is the classification of a text corpus. This is important in many institutions for planning, decision-making, and archives of their projects. Many algorithms exist to automate text classification operations but the most intriguing of them is that which also learns these operations automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables the reduction of the curse of dimensionality in text classification. The classification results are compared with those of conventional text classification models, and it shows that our proposed model has a higher classification performance than conventional models.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

Now when the whole world is still under COVID-19 pandemic, many schools have transferred the teaching from physical classroom to online platforms. It is highly important for schools and online learning platforms to investigate the feedback to get valuable insights about online teaching process so that both platforms and teachers are able to learn which aspect they can improve to achieve better teaching performance. But handling reviews expressed by students would be a pretty laborious work if they were handled manually as well as it is unrealistic to handle large-scale feedback from e-learning platform. In order to address this problem, both machine learning algorithms and deep learning models are used in recent research to automatically process students' review getting the opinion, sentiment and attitudes expressed by the students. Such studies may play a crucial role in improving various interactive online learning platforms by incorporating automatic analysis of feedback. Therefore, we conduct an overview study of sentiment analysis in educational field presented in recent research, to help people grasp an overall understanding of the sentiment analysis research. Besides, according to the literature review, we identify three future directions that researchers can focus on in automatically feedback processing: high-level entity extraction, multi-lingual sentiment analysis, and handling of figurative language.

Conventional data visualization software have greatly improved the efficiency of the mining and visualization of biomedical data. However, when one applies a grid computing approach the efficiency and complexity of such visualization allows for a hypothetical increase in research opportunities. This paper will present data visualization examples presented in conventional networks, then go into higher details about more complex techniques related to leveraging parallel processing architecture. Part of these complex techniques include the attempt to build a basic general adversarial network (GAN) in order to increase the statistical pool of biomedical data for analysis as well as an introduction to the project utilizing the decentralized-internet SDK. This paper is meant to show you said conventional examples then go into details about the deeper experimentation and self contained results.

We propose a novel tensor-based formalism for inferring causal structures from time series. An information theoretical analysis of transfer entropy (TE), shows that TE results from transmission of information over a set of communication channels. Tensors are the mathematical equivalents of these multi-channel causal channels. A multi-channel causal channel is a generalization of a discrete memoryless channel (DMC). We consider a DMC as a single-channel causal channel. Investigation of a system comprising three variables shows that in our formalism, bivariate analysis suffices to differentiate between direct and indirect relations. For this to be true, we have to combine the output of multi-channel causal channels with the output of single-channel causal channels. We can understand this result when we consider the role of noise. Subsequent transmission of information over noisy channels can never result in less noisy transmission overall. This implies that a Data Processing Inequality (DPI) exists for transfer entropy.

We report an easy-to-implement device for SERS-based detection of various analytes dissolved in water droplets at trace concentrations. The device combines an analyte-enrichment system and SERS-active sensor site, both produced via inexpensive and high-performance direct fs-laser printing. Fabricated on a surface of water-repellent polytetrafluoroethylene substrate as an arrangement of micropillars, the analyte-enrichment system supports evaporating water droplet in the Cassie-Baxter superhydrophobic state, thus ensuring delivery of the dissolved analyte molecules towards the hydrophilic SERS-active site. The efficient pre-concentration of the analyte onto the sensor site based on densely-arranged spiky plasmonic nanotextures results in its subsequent label-free identification by means of SERS spectroscopy. Using the proposed device, we demonstrate reliable SERS-based fingerprinting of various analytes, including common organic dyes and medical drugs at ppb concentrations. The proposed device is believed to find applications in various areas, including label-free environmental monitoring, medical diagnostics, and forensics.

Pasteurized milks are still causing food borne illness. Milk contamination can occur at any stage from its way from cow to our tables. Usually milk is pure and sterile when produced in udder of a healthy cow. Like humans, cow are reservoirs of bacteria which are harmless to humans and some cows can harbour few bacteria that are harmful to humans even though they are not harmful to the cow. Milk can be contaminated during or after milking. Also, cow feeds can be contaminated with mycotoxins such as aflatoxins produced by the fungi, Aspergillus flavus. Four types of aflatoxins are known which are; aflatoxin B1, B2, G1, G2. Cows comsuming feeds contaminated with aflatoxin B1 leads to secretion in the milk of aflatoxin M1 and M2 causing aflatoxicosis. Microbial contamination of milk and dairy products is a universal problem and foodborne infections accounting for 20 million cases annually in the world have been identified as an important public health and economic problem in developed as well as developing nations. The main objective of this study was to determine milk microbial quality in Kicukiro district. The specific objectives are to identify bacteria pathogens in milk collected in Kicukiro district, to compare milk quality among sectors of Kicukiro district, to compare milk processed by industries and home-processed milk. The methodology employed in this research was cross-sectional and experimental as the study began with collection of raw data and went through laboratory analysis from July–August, 2018. The findings showed that 59.56% of the milk fell within Grade I – Grade III (< 200,000 ≤ 2,000,000 cfu/ml) and 40.42 % of the milk samples were not within the acceptable limit of total count quality as per COMESA and EAS, non-lactobacilli and fungi were present in most samples as examined through microscope and no Staphylococcus aureus was present in any sample as examined by catalase and coagulase tests.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

The voice contains elementary social communication cues, conveying speech, as well as paralinguistic information pertaining to the emotional state and the identity of the speaker. In contrast to vocal-speech and vocal-emotion processing, voice-identity processing has been less explored. This seems surprising, given the day-to-day significance of person recognition by voice. A valuable approach to unravel how voice-identity processing is accomplished is to investigate people who have a selective deficit in recognising voices. Such a deficit has been termed phonagnosia. In the present chapter, we provide a systematic overview of studies on phonagnosia and how they relate to current neurocognitive models of person recognition. We review studies that have characterised people who suffer from phonagnosia following brain damage (i.e. acquired phonagnosia) and also studies, which have examined phonagnosia cases without apparent brain lesion (i.e. developmental phonagnosia). Based on the reviewed literature, we emphasise the need for a careful behavioural characterisation of phonagnosia cases by taking into consideration the multistage nature of voice-identity processing and the resulting behavioural phonagnosia subtypes.

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like performance from a single trace. We achieve this directly, by training our single-trace detector against labeled events from an array catalog, and by utilizing a deep temporal convolutional neural network. The training data consists of all arrivals in the International Seismological Centre Catalog for seven seismic arrays over a five year window from 1 Jan 2010 to 1 Jan 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 Jan 2015 to 1 Jan 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the transportability and generalization of the technique to new stations. Detection performance against this test set is outstanding. Fixing a type-I error rate of 1%, the algorithm achieves an overall recall rate of 73% on the 141,095 array beam picks in the test set, yielding 102,394 correct detections. This is more than 4 times the 23,259 detections found in the analyst-reviewed single-trace catalogs over the same period, and represents an 8dB improvement in detector sensitivity over current methods. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to be able to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel in the Mill Test Certificate (MTC) and thus support decision-making about the supplier selection or the acceptance of a material supply. Multivariate second-order polynomial model and feedforward back-propagation Artificial Neural Network (ANN) model were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions. The prediction model is used in a step-by-step decision-making procedure, which enables the trained staff to make a quick decision.

Cardiotoxicity is an unwanted side-effect arising from different medications. The term describes a diminution of heart function, affecting especially left ventricular ejection fraction, which can progress to heart failure. Pediatric oncology patients treated with drugs of the anthracycline group, e.g., doxorubicin, can evolve to heart failure patients in adult age. Nuclear Medicine imaging of fatty acid oxidation and glycolysis accurately describes normal and altered heart metabolism. The most common finding is a state of cardiac glycolysis, which is reflected by increased uptake of 18F-desoxyglucose (18F-FDG) at a time when fatty acid oxidation is decreased. Glycolysis has recently been recognized as being the result of coenzyme Q10 deficiency as part of adaptation to hypoxia. Low LEVF correlates with impaired fatty acid oxidation. This deficiency can arise from chemotherapy and radiation and can be corrected by supplementation. Drugs like enalapril can improve fatty acid utilization. We will review relevant Nuclear Medicine imaging studies as well as fundamental biochemical data that demonstrate this pathogenetic path.

Swimming pools are examples of water-intensive facilities, where solutions for reducing economic and environmental costs are increasingly frequently searched. One of the solutions is the recovery of water from wastewater, including from washings obtained through the process of rinsing filter bed. The study objective was the qualitative and quantitative assessment of post-coagulation sludges, the main pollutant found in the washings. During the analyses, assessment of the sedimentation capabilities of the sludges was performed (gravitationally), particle size distribution was assessed (particle size distribution analyzer) and assessment of phytotoxicity with the use of plant indicators in short-term tests was performed (Lemna minor, Lepidium sativum, Sinapis alba, Raphanus sativus). The samples were collected from two independent circulations, which differed in terms of capacity and type of coagulant used. The tested post-coagulation sludges were characterized by high content of total suspended solids: in samples from Circulation 1 from 251 to 128 mg/l, in Circulation 2 from 489 to 228 mg/l. However, the sedimentation processes enabled significant separation of sludges. The hydrolyzed coagulant contributed to the improvement of sedimentation capabilities of sludges. Despite the fact that in many samples low sludge concentrations favored stimulation of plant growth, the post-coagulation sludges can constitute a hazard to plant growth, particularly in the long-term perspective.

In order to study the petrogenesis and tectonic setting of Permian A-type granites and their relationships with hydrothermal mineralization along the Hegenshan-Heihe suture zone (HHSZ) in northeastern China, we select the newly discovered Hongyan Cu-polymetallic deposit in the northeastern part of the HHSZ that develops three stages of mineralization associated with the Shanshenfu alkali-feldspar granite (SAFG). The zircon U-Pb dating and whole rock geochemistry suggest that the SAFG is a typical A-type granite formed in the Early Permian. The zircon Hf isotopes and trace elements suggest that the SAFG has high Ti-in-zircon temperature (721–990℃), high magmatic oxygen fugacity and largely positive εHf(t) (+6.0 to +9.9). Therefore, we propose that the SAFG was derived from the crustal assimilation and fractional crystallization of the charnockitized juvenile crust. The high oxygen fugacity favors the chalcophile elements (e.g., Cu, Au, Ag) of the source region enriched in the fluid phases after magmatic fractional crystallization, consequently facilitating subsequent hydrothermal mineralization, which is also consistent with the characteristics of ore-forming fluids that changed from the initial high temperature, high salinity, high fO2 and CO2-rich magmatic-hydrothermal fluids of stage I to CO2-poor, dilute, and cooling meteoric fluids of stage III. Combined with regional geological background, the Permian A2-type granites along the HHSZ can be formed in post-collisional slab break-off process. In subsequent exploration for hydrothermal deposits along the HHSZ, the Permian A-type granites with arc-related juvenile crustal source and high fO2 have great potential and need more attention.

This article discusses how, in today’s world of disruptive and dramatic social change (DSC), non-sports related coaching, which includes a wide range of services such as life coaching, career coaching, executive coaching, and team coaching, can inadvertently fuel undesirable social dynamics. There is little or no awareness of this risk among coaches and coachees. The global, fast-growing, multi-billion-dollar industry aimed at supporting people and organisations to perform better and increase wellbeing while managing and adapting to change has been developed with limited sociological input. The article is based on 15 years of social constructionist-informed reflective practice by a sociologist-turned-coach, and uses a multi-layered autoethnographic account to argue for a sociologically informed paradigm shift in coaching, as well as relevant sociological knowledge and social and human sciences research methodology in coaches’ education. It presents the rationale and key features of a new coaching approach that places DSC-relevant sociological concepts at the heart of the coaching process, helps people develop psycho-sociological awareness, and uses a learning-to-develop through research design. A new definition of coaching to address DSC is derived. Coaching practitioners will find DSC-relevant sociological concepts, critical reflections, coaching questions, and procedures to expand coaching effectiveness. Interdisciplinary research topics combining coaching sociology and coaching psychology are suggested.

Gender recognition is trivial for physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during the stepping exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SMO). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Nazarloo's work (90.34%) and other classifiers.

Post-training quantization is pivotal in deploying convolutional neural networks for mobile applications. Block-wise reconstruction with adaptive rounding, as employed in prior works like BrecQ and Qdrop, facilitates acceptable 4-bit quantization accuracy. However, adaptive rounding is time-intensive, and its constraint on weight optimization space curtails the potential for quantization performance. The optimality of block-wise reconstruction hinges on the quantization status of subsequent network blocks. In this investigation, we delve into the theoretical underpinnings of the limitations inherent in adaptive rounding and block-wise reconstruction. Our exploration leads to the development of a post-training quantization methodology, designated as AE-Qdrop. This algorithm operates in two distinct phases: block-wise reconstruction and global fine-tuning. The block-wise reconstruction phase introduces a progressive optimization strategy, superseding adaptive rounding, which not only augments quantization precision but also significantly improves quantization efficiency. To mitigate the risk of overfitting, we introduce a random weighted quantized activation mechanism. During the global fine-tuning phase, we account for interdependencies among quantized network blocks. The weight of each network block will be corrected with logit matching and feature matching. Extensive experiments validate that AE-Qdrop achieves high-precision and efficient quantization. For instance, in the case of 2-bit MobileNetV2, AE-Qdrop outperforms Qdrop by achieving a 6.26% enhancement in quantization accuracy and quintupling the quantization efficiency.

SARS-CoV-2 mRNA vaccination can entail chronic fatigue/dis-autonomy tentatively termed post-acute COVID-19 vaccination syndrome (PACVS). We explored receptor autoantibodies and interleukin-6 (IL-6) as somatic correlates of PACVS. Blood markers determined before and six months after first-time SARS-CoV-2-vaccination of healthy controls (N = 89, 71 females, mean/ median age 39/ 49 years) were compared with corresponding values of PACVS-affected persons (N= 191, 159 females, mean/median age: 40/39 years) exhibiting chronic fatigue/dis-autonomy (≥ three symptoms for ≥ five months after last SARS-CoV-2 mRNA vaccination) not due to SARS-CoV-2 infection and/or confounding diseases/medications. Normal vaccination response encompassed decreases of 11 receptor-antibodies (by 25 - 50 %, p < 0.0001), increases in two receptor-antibodies (by 15 - 25 %, p < 0.0001) and normal IL-6. In PACVS, serological vaccination-response appeared significantly (p < 0.0001) altered, allowing discrimination from normal post-vaccination state (sensitivity = 90%, p &lt; 0.0001) by increased angiotensin II type 1 receptor antibodies (cut-off ≤ 10.7 U/ml, ROC-AUC = 0.824 ± 0.027), decreased alpha-2B adrenergic receptor antibodies (cut-off ≥ 25.2 U/ml, ROC-AUC = 0.828 ± 0.025) and increased IL-6 (cut-off ≤ 2.3 pg/ml, ROC-AUC = 0.850 ± 0.022). PACVS is thus indicated as a somatic syndrome delineated/detectable by diagnostic blood markers

: This study was aimed at assessing adaptation and bonding of discontinuous (short) glass fiber-reinforced composite to intraradicular dentin EverX Flow (GC Corporation, Tokyo, Japan), when used as intracanal composite filling and anchorage instead of traditional fiber posts. (2) Methods: Seventy intact extracted human teeth were endodontically treated and randomly divided into 6 groups (n=10), depending on the materials used in the post space. In Group 1, a 2-bottle universal adhesive G2 Bond Universal + EverX Flow were tested. In group 2, a single-component universal adhesive G-Premio Bond + EverX Flow were used. In groups 3 and 4 the same materials are tested, but after cleaning of the canal walls with 17% EDTA and final irrigation with 5.25% NaOCl Ultrasound Activated. In the last three Groups (5-7) traditional prefabricated GC Fiber Posts 1.6 mm silanized with G-Multi Primer for 1 minute are cemented with a dual-cured composite resin cement (GradiaCore), after ultrasonic irrigation in the groups 6 and 7. In each group, 1 mm-thick slices from each sample (n=10) were cut for light microscope and SEM inspection for study materials adaption to the dentin and for measuring push-out strength of post / cemement material to the dentin / prefabricated post. These results were statistically analyzed: as the data distribution was not normal, the Kruskal-Wallis Analysis of Variance by Ranks had to be applied. The level of significance was set at p<0.05. Results: Push-out forces varied between 6.66-8.37 MPa. No statistically significant differences were recorded among the groups. Microscopic examination showed that ultrasonic irri-gation increased adaptation of the materials to the dentin surface. There was a trend of higher bond strength among the tested groups when EverX Flow was used. Also, the type of failure was more often cohesive when ultrasonic irrigation and two-step adhesive system were used. Conclusions: Within the limitations of this in vitro study, it may be concluded that when EverX Flow was used for intracanal anchorage in the post-endodontic recon-struction, similar push-out retentive forces and strength to those of traditional fiber posts cemented with particulate filler resin composite cements were achieved. Although further studies are necessary, EverX Flow represents an effective alternative to traditional fiber post adhesion in particular when used in combination with the two-step adhesive system and ultrasonic activation.

Background: The annual mortality of patients with untreated chronic thromboembolism pulmonary hypertension (CTEPH) is approximately 50% unless a timely diagnosis is followed by adequate treatment. In pulmonary embolism (PE) survivors with functional limitation the diagnostic work-up starts with echocardiography. It is followed by lung scintigraphy and right heart catheterization. However, noninvasive test providing diagnostic clues to CTEPH, or ascertain this diagnosis as very unlikely, would be extremely useful since the majority of post PE functional limitations is caused by deconditioning. Methods: Patients after acute PE underwent a structured clinical evaluation with electrocardiogram, routine laboratory tests including NT-proBNP and echocardiography. The aim of study was to verify whether the parameters from echocardiographic or perhaps electrocardiographic examination and NT-proBNP concentration best determine the risk of CTEPH. Results: A total (n = 261, male n = 123) patients after PE were included into the study, in group of 155 patients (59.4%) with reported functional impairment 13 patients (8.4%) had CTEPH and 7 PE survivors had chronic thromboembolic pulmonary disease (CTEPD) (4,5%). Echo parameters differed significantly between CTEPH/CTEPD cases and other symptomatic PE survivors. Patients with CTEPH/CTEPD had also higher level of NT-proBNP (p = 0.022) but concentration of NT-proBNP above 125 pg/ml did not differentiate patients with CTEPH/CTEPD (p>0.05). Additionally, proportion of patients with right bundle brunch block registered in ECG was higher in group with CTEPH/CTED (23.5% vs. 5.8%, p = 0.034) but there were no differences between other ECG characteristics of right ventricle overload. Conclusion: Screening for CTEPH/CTEPD should be performed in patients with reduced exercise tolerance compared to pre PE period, It is not effective in asymptomatic PE survivors. Patients with CTEPH/CTED had predominantly abnormalities indicatingchronic thromboembolism in the echocardiographic assessment. NT-proBNP and electrocardiographic characteristics of right ventricle overload proved to be insufficient in predicting CTEPH/CTEPD development.

Background: Globally, infections are the third leading cause of neonatal mortality. Predominant risk factors for facility-born newborns are poor hygiene practices that span both the facility and home environments. Current improvement interventions focus on only one environment and tar-get limited caregivers, primarily birth attendants and mothers. To inform the design of a hand hygiene behaviour change intervention in rural Cambodia, a formative mixed-methods research study was conducted to investigate the context specific behaviours and determinants of hand-washing among healthcare workers, maternal and non-maternal caregivers along the early new-born care continuum. Methods: Direct observations of hygiene practices of all individuals providing care to 46 newborns across eight facilities and associated communities were completed and hand hygiene compliance assessed in analysis. Semi structured interactive interviews were subsequently conducted with 35 midwives and household members to explore the corresponding cognitive, emotional, and environmental factors influencing the observed key hand hygiene behaviours. Results: Hand hygiene opportunities during newborn care were frequent in both set-tings (n = 1319) and predominantly performed by mothers, fathers and non-parental caregivers. Compliance to hand hygiene protocol across all caregivers, including midwives, was inadequate (0%). Practices were influenced by the lack of accessible physical infrastructure, time, increased workload, low infection risk perception, nurture-related motives, norms and inadequate knowledge. Conclusion: Our findings indicate that an effective intervention in this context should be multi-modal to address the different key behaviour determinants and target a wide range of caregivers.

Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.

Multiphase systems are important in minerals processing, and usually include solid-solid and solid-fluid systems. Examples of operations in multiphase systems include flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization, in multiphase systems, are analyzed. Different scales of size and time are included, and therefore the analysis includes modeling at the molecular level and unit operation level, and the application of optimization for the design of a plant. New strategies for the modeling and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The paper finishes with tools to study the uncertainty, both epistemic and stochastic, which is present in all mineral processing operations. It is shown that all these areas are very active and can help to understand, operate, design, and optimize mineral processing that involves multiphase systems.

This proposal was prepared in the very first weeks of 2020 because of the outbreak of COVID-19.There is good reason to suppose that rotavirus vaccine can be used as protection tool to effectively and safely fight and mitigate SARS-CoV-2 infection and the impact caused by COVID-19 in adult humans, due to the development of cross and trained immunity following rotavirus vaccination. Up-to-date, some rotavirus vaccines are available and approved, two of them have a large experience in results and safety. Little experience has been achieved in the use of rotavirus vaccine in adults. However, it can be expected that it would be safe and effective in adults and in the elderly as well. This proposal explains the background.

Mediterranean climate is marked by arid climate conditions in summer, therefore, crop irrigation is crucial to sustain plant growth and productivity in this season. If groundwater is utilized for irrigation, an impressive water pumping is needed to satisfy crop water requirements at catchment scale. Consequently, irrigation water quality gets worse, specifically considering groundwater salinization near the coastal areas due to seawater intrusion, also triggering soil salinization. With reference to an agricultural coastal area in the Mediterranean basin (Southern Italy), close to the Adriatic sea, an assessment of soil salinization risk due to processing tomato cultivation was carried out. A simulation model was arranged to perform, on daily basis, a water and salt balance along the soil profile. Long-term weather data and soil physical parameters representative of the considered area were utilized in applying the model, also considering three salinity levels of irrigation water. Based on the climatic analysis performed and the model outputs, the probability of soil salinity came out very high, such as to seriously threaten tomato yield. Autumn-winter rainfall resulted frequently insufficient to leach excess salts away from the soil profile and reach sustainable conditions of tomato cultivation. Therefore, alternative cropping strategies were prospected.

“You should know the words by the company they keep!” has been one of the most famous slogans attributed to John Rubert Firth, 1957. This has ignited a whole school in linguistic research known as the British empiricist contextualism. Sixty years later, many un- or semi-supervised machine learning algorithms have been successfully designed and implemented aiming at extracting word meaning from within the context of a text corpus. These algorithms treat words, more or less, as vectors of real numbers representing frequencies of word occurrences within context and word meaning as positions of words in a high-dimensional vector space model. Word associations, in turn, are treated as calculated distances among them. With the rise of Deep Learning (DL) and other artificial neural networks based architectures, learning the positioning of words and extracting word associations as measured by their distances has further improved. In this paper, however, we revisited the main stream of algorithmic approaches and set the stage for a partly cross-disciplinary evaluation framework to judge about the nature of the extracted word associations by state-of-the-art machine learning algorithms. Our preliminary results are based on word associations extracted from the application of DL framework on a Google News text corpus, as well as on comparisons with human created word association lists such as word collocation dictionaries and psycholinguistic experiments. The results and conclusions provide some insights into the inherited limitations in interpreting the type of word associations and underpinning relations between words with inevitable consequences in other areas, such as extraction of knowledge graphs or image understanding.

In this paper, we present a novel approach for managing the file system in Linux using a voice assistant. Our system allows users to perform file system operations such as creating directories, renaming files, and deleting files by issuing voice commands. We develop a voice assistant using Python libraries and integrate it with the file system in Linux. The voice assistant is capable of understanding natural language and executing commands based on the user's voice inputs. We conduct experiments to evaluate the performance of the system and demonstrate that our approach is effective and efficient in managing the file system using voice commands. Our system can enhance the accessibility and usability of the file system in Linux for individuals with disabilities or those who prefer a hands-free approach to file management.

Regional anesthesia in postoperative pain management have developed in recent years, especially with the advent of fascial plane blocks. This study aims to compare the ultrasound-guided bilateral erector spinae plane block (ESPB) versus bilateral transversus abdominis plane block (TAPB) on postoperative analgesia after laparoscopic or robotic urologic surgery. This was a retrospective observational study; 97 patients (ESPB-group) received bilateral ultrasound-guided ESPB with 20 mL of ropivacaine 0.375% plus 0.5 mcg/kg of dexmedetomidine in each side at the level of T7-T9, and 93 patients (TAPB-group) received bilateral ultrasound-guided TAPB with ropivacaine 0.375% or 0.25%. The primary outcome was post-operative numeric rating scale (NRS) pain score, which was significantly lower in the ESPB-group on postoperative days 0, 2, and 3 (p=0.012, p=0.016 and p=0.004). Concerning the secondary outcomes, consumption of ropivacaine was significantly lower in ESPB-group (p<0.001) and the total amount of analgesic rescue doses was significantly lower in the ESPB-group than TAPB-group in postoperative days from 2 to 4 (1 vs 3, p>0.001). Incidence of postoperative nausea and vomiting was higher in the TAPB-group and no block related complications were observed. We conclude that ESPB provide better postoperative pain control with less consumption of local anesthetic and less total rescue analgesic therapies.

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domains embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs. Finally, to support similar research and development in this field centered around the analysis of tweets, we have developed an open-access Twitter dataset that comprises tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.

In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.

Abstract: Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems, e.g., the dynamics of the wildland-urban interface. Herein, we propose a framework that jointly uses remote sensing derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach for two U.S. study sites against historical settlement extents derived from the Historical Settlement Data Compilation for the US, HISDAC-US, achieving Area-under-the-Curve values >0.9. Our results are largely in agreement with model-based urban areas from the HYDE database, and demonstrate that the integration of remote sensing derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization, and long-term land cover change in countries where historical maps are available.

Despite the development of technologies, modern methods of disposal of end-of-life tires most often represent either the incineration in cement kilns or the destruction of tires in special landfills, showing a lack of sustainable recycling of this valuable material. The fundamental role of recycling is evident, and the development of high-efficiency processes represents a priority for the European market. Therefore, investigation of end-of-life rubber processing methods is of high importance for manufacturers and recyclers of rubber materials. In this paper, methods of processing of end-of-life tires are reviewed in order to obtain rubber crumb, which can later be used in the production of new industrial rubber goods and composite The processes of separation end-of-life tire into fractions by type of materials using mechanical processing methods along with mechanochemical and mechanical processes of processing the materials of used tires in order to obtain crumb rubber of various fractions and chemical reactivity are considered.

In this work, we studied light-output properties, efficiency function as well as PSD capability of p-Terphenyl scintillator. The selected solid cylindrical scintillation detector has a size of (45 x 45) mm with PSD properties. Recently presented studies of light-output functions have been measured only for low neutron energies. Our motivation has been to determine more accurate the light output function for p-Terphenyl scintillator over a wider neutron energy range.

Sensory Processing Sensitivity (SPS) is a trait describing inter-individual differences in sensitivity to environments, both positive and negative ones. SPS has attracted growing societal interest. However, (neuro)scientific evidence is lagging behind. We critically discuss how to measure SPS, how it relates to other theories of Environmental Sensitivity and other temperament and personality traits, how SPS interacts with environments to influence (a)typical development, what the underlying aetiologies and mechanisms are, and its relation to mental disorders involving sensory sensitivities. Drawing on the diverse expertise of the authors, we set an agenda for future research to stimulate the field. We conclude that SPS is a heritable, evolutionarily conserved trait, linked to increased risk for psychopathology and stress-related problems in response to negative environments, as well as to greater benefits (e.g., intervention responsivity, positive mood) in positive environments. We need advances in objective assessment of SPS, understanding mechanisms, differentiating it from (seemingly) related mental disorders, to exploit the potential of SPS to improve mental health, preserve human capital, and prevent adverse effects.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

This study presents a novel approach to enhance Large Language Models (LLMs) like Alpaca by dynamically integrating real-time information. This method addresses the issue of content hallucination and data relevancy by automatically collecting and integrating current data from credible sources into model prompts. Experiments show a significant improvement in accuracy and a decrease in content hallucination, with a manageable increase in response time. The research underscores the potential of real-time data integration in making LLMs more accurate and contextually relevant, setting a foundation for future advancements in dynamic data processing in AI.

In order to provide high capacity and universal access of telecommunication networks, this paper reviews and prospects the advanced multiplexing technology, physical layer digital signal processing technology, infrastructure sharing technology, security protection technology, intelligent control management and other key technologies for beyond 100G next generation passive optical network (NG-PON).

Large-area nanostructuring of glasses using intense laser beam remains a difficult task due to the extreme non-linear absorption of the laser energy by the material. Precise optimization of the process parameters is essential for fabricating nanostructures with large area coverage. In this study, we report the findings on creating high spatial frequency LIPSS (HSFL) on borosilicate glass through direct laser writing, using a femtosecond laser with a wavelength λ = 800 nm, pulse duration τ = 35 fs, and repetition frequency frep = 1 kHz. The orientation of the HSFL was found to be parallel to the electric field vector. We measured the single pulse ablation threshold (Fth=3.87±0.26 J/cm2) and incubation factor (S=0.68±0.03) of Borosilicate glasses for precise control for large area surface structuring. Single-spot experiments indicate that uniform LIPSS formation is limited by melt formation inside the irradiated area for higher fluence and a larger number of irradiated laser pulses. The orientation of the scan axis with the laser beam polarization is found to be significantly influencing the uniformity of the large area processing. We found that the orientation of the scan axis with the laser beam polarization significantly affects the uniformity of large-area processing, with redeposition and melt formation being higher when the scan axis is perpendicular to the laser beam polarization. Large-area processing of the borosilicate glass surface is done by line-by-line scanning over the surface with a scan orientation parallel to the laser beam polarization. The optical characterization reveals that the transmittance and reflectance of the borosilicate glass decreased significantly after processing. Also, the wettability of the surface has been changed from hydrophilic to super hydrophilic after processing. These chemical contamination-free and uniformly distributed structures have potential applications in optics, microfluidics, photovoltaics, and biomaterials.

Colonoscopy is used for colorectal cancer (CRC) screening. Extracting details of the colonoscopy findings from free text in electronic health records (EHRs) can be used to determine patient risk for CRC and colorectal screening strategies. In this study, we developed and evaluated the accuracy of a deep learning model framework to extract information for the clinical decision support system to analyze relevant free-text colonoscopy reports, including indications, pathology, and findings notes. The Bio-Bi-LSTM-CRF framework was developed using Bidirectional Long Short-term Memory (Bi-LSTM) and Conditional Random Fields (CRF) to extract several clinical features from these free-text reports, including indications for the colonoscopy, findings during the colonoscopy, and pathology of the resected material. We then trained the Bio-Bi-LSTM-CRF and existing Bi-LSTM-CRF models on 80% of 4,000 manually annotated notes obtained from the colonoscopy reports of 3,867 patients. The clinical notes were from a group of patients aged over 40 years old enrolled in four Veterans Affairs Medical Centers. A total of 10% of the remaining annotated notes were used to train hyperparameter, while the remaining 10% were used to evaluate the accuracy of our model (Bio-Bi-LSTM-CRF) and to compare the results with the outcomes obtained using Bi-LSTM-CRF. The results of our experiments showed that the bidirectional encoder representations by integrating dictionary function vector from Bio-Bi-LSTM-CRF and strategies character sequence embedding approach is an effective way to identify colonoscopy features from EHR-extracted clinical notes. Therefore, the Bio-Bi-LSTM-CRF model is concluded to be capable of creating new opportunities to identify patients at risk for colon cancer and to study their health outcomes.

Recent developments in mid-infrared (MIR) spectroscopic ellipsometry enabled by quantum cascade lasers (QCLs) resulted in a drastic improvement in signal-to-noise ratio compared to conventional thermal emitter based instrumentation. Thus, it was possible to reduce the acquisition time for high-resolution broadband ellipsometric spectra from multiple hours to less than 1 second. This opens up new possibilities for real-time in-situ ellipsometry in polymer processing. To highlight these evolving capabilities we demonstrate the benefits of a QCL based MIR ellipsometer by investigating single and multilayered polymer films. The molecular structure and reorientation of a 2.5m thin biaxially oriented polyethylene terephtalate film is monitored during a stretching process lasting 24.5 s to illustrate the perspective of ellipsometric measurements in dynamic processes. In addition, a polyethylene/ethylene vinyl alcohol/polyethylene multilayer film is investigated at continuously varying angle of incidence ( 0∘ – 50∘) in 17.2 s, highlighting an unprecedented sample throughput for the technique of varying angle spectroscopic ellipsometry in the MIR spectral range. The obtained results underline the superior spectral and temporal resolution of QCL ellipsometry and qualify this technique as suitable method for advanced in-situ monitoring in polymer processing.

The main purpose of bovine colostrum, being the milk secreted by a cow after giving birth, is to transfer passive immunity to the calf. The calves have an immature immune system as they lack immunoglobulins (Igs). Subsequently, the supply of good quality bovine colostrum is required. The quality of colostrum is classified by low bacterial counts and adequate Ig concentrations. Bacterial contamination can contain a variety of human pathogens or high counts of spoilage bacteria, which becomes more challenging with emerging use of bovine colostrum as food and food supplements. There is also a growing risk for the spread of zoonotic diseases originating from bovines. For this reason, processing based on heat treatment or other feasible techniques are required. This review provides an overview of literature on the microbial quality of bovine colostrum and processing methods to improve its microbial quality and keep its nutritional values as food. The highlights of this review are: high quality colostrum is a valuable raw material in food products and supplements, the microbial safety of bovine colostrum is increased using appropriate processing-suitable effective heat treatment, which does not destroy the high nutrition value of colostrum, the heat treatment processes are cost-effective compared to other methods, and heat treatment can be performed in both small- and large-scale production.

A more sustainable society and economy also implies a more sustainable behavior in the consumption of financial products. A possible change in focus can come from the demand side, so that more sustainable consumption of financial products have to go hand to hand with financial literacy. However, financial literacy, potential favoring of this sustainable behavior, is far from reaching an international consensus about its definition, object and scope. Two objectives are analyzed; the different interpretations of financial literacy in the academic literature, as well as its evolution and how, in what context and with what other concepts the term is used in social networks. Scientometric techniques and content analysis have been used to carry out a systematic review of literature and also NLP to analyze the comments on Social Networks. Critical moments are identified in the definition of financial literacy. Also ten sentiments are analyzed in social networks in wich positivity, trust, and anticipation predominate. Greater attention to this issue is necessary both from the private initiative and from public policies, so that financial literacy is an effective tool for a more sustainable behavior by consumers. Finally, a new definition is proposed based on our findings.

Progression of oral squamous cell carcinoma (OSCC) has been associated with an escape of tumor cells from the host immune surveillance due to an increased knowledge of its underlying molecular mechanisms and its modulation by the tumor microenvironment and immune cell repertoire. In this study the expression of HLA class I (HLA-I) antigens and of components of the antigen processing machinery (APM) was analyzed in 160 pathologically classified human papilloma virus (HPV)-negative OSCC lesions and correlated to the intra-tumoral immune cell response, IFN- signaling and to the patients outcome. A heterogeneous, but predominantly lower constitutive protein expression of HLA-I APM components was found in OSCC sections when compared to non-neoplastic cells. Tumoral HLA-I APM component expression was further categorized into the three major phenotypes HLA-Ihigh/APMhigh, HLA-Ilow/APMlow and HLA-Idiscordant high/low/APMhigh. In the HLA-Ihigh/APMhigh group, the highest frequency of intra-tumoral CD8+ T cells and lowest number of CD8+ T cells close to FoxP3+ cells was found. Patients within this group presented the most unfavorable survival, which was significantly evident in stage T2 tumors. Despite a correlation with the number of intra-tumoral CD8+ T cells, tumoral JAK1 expression as a surrogate marker for IFN- signaling was not associated with HLA-I/APM expression. Thus, the presented findings strongly indicate the presence of additional factors involved in the immunomodulatory process of HPV-negative OSCC with a possible tumor-burden-dependent complex network of immune escape mechanisms beyond HLA-I/APM components and T cell infiltration in this tumor entity.

Progression of oral squamous cell carcinoma (OSCC) has been associated with an escape of tumor cells from the host immune surveillance with growing evidence of its underlying molecular mechanisms and its interaction with the immune cell control. In this study the expression of HLA class I (HLA-I) antigens and of components of the antigen processing machinery (APM) was analyzed in 160 consecutive human papilloma virus (HPV)-negative OSCC lesions and correlated to tumor specific parameters, the intratumoral immune cell response and to the patients outcome. A heterogeneous, but predominantly lower constitutive protein expression of HLA-I APM components was seen in OSCC sections when compared to non-neoplastic cells. Based on the expression levels of HLA-I APM components three main OSCC subgroups were detected and categorized into HLA-Ihigh/APMhigh, HLA-Ilow/APMlow and HLA-Idiscordant high/low/APMhigh phenotypes. In the HLA-Ihigh/APMhigh group, the highest frequency of intratumoral CD8+ T cells and lowest number of CD8+ T cells close to FoxP3 cells was found. Despite being associated with the highest T cell infiltration, patients within this group presented the most unfavorable survival, which was most evident in stage T2 tumors. Thus, the presented findings strongly indicate the presence of additional factors involved in the immunomodulatory process of HPV-negative OSCC with a possible tumor-burden-dependent complex network of immune escape mechanisms beyond HLA-I/APM components and T cell infiltration in this tumor entity.

Real data obtained from ship in operation are processed and analyzed in this paper. The intention was to provide software which would predict ship’s fuel consumption in some future time instant. It is showed that it is possible to develop such software based on numeric fitting of known data. In order to check how well the prediction of future fuel consumption is, we used only the first half of data for obtaining prediction curve. The second part of data was used to compare different prediction curves goodness. Hence, the presented research used actually a “real future data” and forecasted future data, which are used to numerically evaluate goodness of prediction. The research is of interest for companies logistics, to provide adequate fuel for fleet when and where actually needed. It is concluded that there are several prediction functions which satisfy used statistical quality measures.

Renewable resources gain increasing interest as source for environmentally benign biomaterials, such as drug encapsulation/release compounds, and scaffolds for tissue engineering in regenerative medicine. Being the second largest naturally abundant polymer, the interest in lignin valorization for biomedical utilization is rapidly growing. Depending on resource and isolation procedure, lignin shows specific antioxidant and antimicrobial activity. Today, efforts in research and industry are directed toward lignin utilization as renewable macromolecular building block for the preparation of polymeric drug encapsulation and scaffold materials. Within the last five years, remarkable progress has been made in isolation, functionalization and modification of lignin and lignin-derived compounds. However, literature so far mainly focuses lignin-derived fuels, lubricants and resins. The purpose of this review is to summarize the current state of the art and to highlight the most important results in the field of lignin-based materials for potential use in biomedicine (reported in 2014–2018). Special focus is drawn on lignin-derived nanomaterials for drug encapsulation and release as well as lignin hybrid materials used as scaffolds for guided bone regeneration in stem cell-based therapies.

Proteins are the workhorses of the cell that perform biological functions by interacting with other proteins. Many statistical methods for protein-protein interaction (PPI) have been studied without considering time-dependent changes in networks and the functionalities. I introduced a novel method that models PPI networks as being dynamic in nature and evolving time-varying multivariate distribution with Conditional Random Fields (CRF). This research is directed towards implementing this new combinatorial algorithm on massively parallel architectures such as Graphics Processing Units (GPUs) for efficient computations for large scale bioinformatics datasets. I compared Conditional Random Fields (CRF) and the proposed novel method using CRF combined with the Block Coordinate Descent algorithm for human protein-protein interaction data set. Both are implemented on GPU-Accelerated Computing Architecture and the proposed novel method showed the advantages in predicting protein-protein interaction sites. I also show that the proposed approach is more efficient in 6.13% than standalone CRF++ in predicting protein-protein interaction sites.

Objectives: Studies evaluating machine learning (ML) algorithms on cross-lingual RadLex® mappings for developing context-sensitive radiological reporting tools are lacking. Therefore, we investigated whether ML-based approaches can be utilized to assist radiologists in providing key imaging biomarkers – such as The Alberta stroke programme early CT score (APECTS). Material and Methods: A stratified random sample (age, gender, year) of CT reports (n=206) with suspected ischemic stroke was generated out of 3997 reports signed off between 2015-2019. Three independent, blinded readers assessed these reports and manually annotated clinico-radiologically relevant key features. The primary outcome was whether ASPECTS should have been provided (yes/no: 154/52). For all reports, both the findings and impressions underwent cross-lingual (German to English) RadLex®-mappings using natural language processing. Well-established ML-algorithms including classification trees, random forests, elastic net, support vector machines (SVMs) and boosted trees were evaluated in a 5 x 5-fold nested cross-validation framework. Further, a linear classifier (fastText) was directly fitted on the German reports. Ensemble learning was used to provide robust importance rankings of these ML-algorithms. Performance was evaluated using derivates of the confusion matrix and metrics of calibration including AUC, brier score and log loss as well as visually by calibration plots. Results: On this imbalanced classification task SVMs showed the highest accuracies both on human-extracted- (87%) and fully automated RadLex® features (findings: 82.5%; impressions: 85.4%). FastText without pre-trained language model showed the highest accuracy (89.3%) and AUC (92%) on the impressions. Ensemble learner revealed that boosted trees, fastText and SVMs are the most important ML-classifiers. Boosted trees fitted on the findings showed the best overall calibration curve. Conclusions: Contextual ML-based assistance suggesting ASPECTS while reporting neuroradiological emergencies is feasible, even if ML-models are restricted to be developed on limited and highly imbalanced data sets.

Soft-Core architecture for Analogue to Digital Converter (ADC) sampling is useful for mixed signal applications. Soft-core architecture for cutting edge odd or even ADC sampling with interface to block RAM memory has not been found. Soft-core architecture as a concept has become popular due to the advantage of customization for different applications as compared to general-core architecture suited for single application. The latest generation of piece wise sampling is odd sampling and was introduced in the second decade of the 20th century. Odd and even order sampling techniques are analogue in nature driven by a tuned (tuned for odd or even) mixer. This paper proposes a third-generation piece wise sampling with soft-core architecture that enables an option to select both odd and even while interfacing to memory mapping. The proposed odd/even has superior SNR performance of 6 dB as compared to existing architecture such as Mod-∆ which recorded worst performance of 18 dB. Advances in soft-core technology have allowed a niche odd/even switching field to be identified and studied, the study has also been extended to include memory architecture.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex disease characterized by profound fatigue, post-exertional malaise (PEM), and neurocognitive dysfunction. Immune dysregulation and gastrointestinal symptoms are commonly observed in ME/CFS patients. Despite affecting approximately 0.89% of the general population, the underlying pathophysiological mechanisms remain poorly understood. This study aimed to elucidate the relationship between immunological characteristics and intestinal barrier function in ME/CFS patients. ME/CFS patients were stratified into two groups based on their immune competence. After documentation of detailed medical records, serum and plasma samples were collected for assessment of inflammatory immune mediators and biomarkers for intestinal barrier integrity by ELISA. We found reduced complement protein C4a levels in immunodeficient ME/CFS patients suggesting a sub-group specific innate immune dysregulation. ME/CFS patients without immunodeficiencies exhibit a mucosal barrier leakage, as indicated by elevated levels of Lipopolysaccharide-binding protein (LBP). Stratifying ME/CFS patients based on immune competence enabled the distinction of two subgroups with different pathophysiological patterns. The study highlights the importance of emphasizing precise patient stratification in ME/CFS, particularly in the context of defining suitable treatment strategies. Given the substantial health and socioeconomic burden associated with ME/CFS, urgent attention and research efforts are needed to define causative treatment approaches.

This study introduces a specialized Automatic Speech Recognition (ASR) system, leveraging the Whisper Large-v2 model, specifically adapted for radiological applications in French language. Our methodology focused on adapting the model to accurately transcribe medical terminology and diverse accents within the French language context, achieving a notable Word Error Rate (WER) of 17.121%. The research involved extensive data collection and preprocessing, utilizing a wide range of French medical audio content. The results demonstrate the system's effectiveness in transcribing complex radiological data, underscoring its potential to enhance medical documentation efficiency in French-speaking clinical settings. The discussion extends to the broader implications of this technology in healthcare, including its potential integration with Electronic Health Records (EHRs) and its utility in medical education. The study also explores future research directions, such as tailoring ASR systems to specific medical specialties and languages. Overall, this research contributes significantly to the field of medical ASR systems, presenting a robust tool for radiological transcription in the French language and paving the way for advanced technology-enhanced healthcare solutions.

According to the current European and Italian scenario related to urban re-generation, cultural and landscape heritage, valorisation is being also enhanced by the activation of innovative processes. These involve the development of methodologies and tools that are able to address decision-making processes among low entropy economy, complex values and creative practices. In this perspective, the research aims to investigate the possibilities of developing a Cultural Heritage Low Entropy Enhancement (CHLEE) approach by considering how the complex values of cultural heritage can vary not only through a physical transformation of spaces but also through a program of uses and activities able to produce new values, where the human experience is essential. This type of model modifies the objectives that characterise the valorisation of cultural heritage and landscape, recognising that the fruition is no longer “consumerist” but “experiential”. A crucial role is represented by the heterogeneity of creative practices that contribute to the identificationidentifying and implementation ofimplementing innovative management and governance models. The present paper explores the components of creative regenerative processes, based upon the ex-post evaluation of some Italian experiments, across the PROMETHEE-GAIA multi-criteria method, to understand how creative experiences are building innovation ecosystem thanks to low entropy economy and improve the ex-ante evaluation for new strategies and policies.

The review of previous works shows this study is the first attempt to analyse the lockdown effect using Natural Language Processing Techniques, particularly sentiment analysis methods applied at large scale. On the other hand, it is also the first of its kind to analyse the impact of COVID 19 on the university community jointly on staff and students and with a multi-country perspective. The main overall findings of this work show that the most often related words were family, anxiety, house and life. On another front, it has also been shown that staff have a slightly less negative perception of the consequences of COVID in their daily life. We have used artificial intelligence models like swivel embedding and the Multilayer Perceptron, as classification algorithms. The performance reached in terms of accuracy metric are 88.8% and 88.5%, for student and staff respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.

The dominant paradigm holds that spontaneous and therapeutically induced anti-tumor responses are mediated mainly by CD8 T cells and directed against tumor-specific antigens (TSAs). The presence of specific TSAs on cancer cells can only be proven by mass spectrometry analyses. Bioinformatic predictions and reverse immunology studies cannot provide this type of conclusive evidence. Most TSAs are coded by unmutated non-canonical transcripts that arise from cancer-specific epigenetic and splicing aberrations. When searching for TSAs, mass spectrometry analyses must therefore interrogate not only the canonical reading frame of annotated exome but all reading frames of the entire translatome. The majority of aberrantly expressed TSAs (aeTSAs) derive from unstable short-lived proteins that are good substrates for direct MHC I presentation but poor substrates for cross-presentation. This is an important caveat because cancer cells are poor antigen-presenting cells and the immune system therefore depends on cross-presentation by dendritic cells (DCs) to detect the presence of TSAs. We therefore postulate that, in the untreated host, most aeTSAs are undetected by the immune system. We present evidence suggesting that vaccines inducing direct aeTSA presentation by DCs represent an attractive strategy for cancer treatment.

Rett syndrome (RTT), a neurodevelopmental disorder, is mainly caused by mutations in methyl CpG-binding protein 2 (MECP2), which alter the functions of domains to either bind to methylated DNA or interact with a transcriptional co-repressor complex. It has been established that alterations in cyclin-dependent kinase-like 5 (CDKL5) or forkhead box protein G1 (FOXG1) correspond to distinct neurodevelopmental disorders, given that a series of studies have indicated that RTT is also caused by alterations in either one of these genes. We tried to elucidate RTT through evolution and structure assessment of MeCP2, CDKL5, and FOXG1, by focusing on their binding partners and disordered structures. Here, we provide insight into the similarities of the FOXG1 and MECP2 binding partners evolution and function. On the other hand, we suggest that CDKL5 could be a potential candidate for a classical RTT treatment, particularly based on its disordered structure that spans after the catalytic domain to the C-terminus, which shows abundant linear motifs that can bind to molecules with divergent structures of similar affinity. Additionally, we provide insight into the relationship between disordered structure and disease.

The tryptophan (TRP)-kynurenine (KYN) metabolic pathway is a main player of TRP metabolism through which more than 95% of TRP is catabolized. The pathway is activated by acute and chronic immune responses leading to a wide range of illnesses including cancer, immune diseases, neurodegenerative diseases, and psychiatric disorders. The TRP-KYN pathway synthesizes multifarious metabolites including oxidants, antioxidants, neurotoxins, neuroprotectants, and immunomodulators. The immunomodulators are known to facilitate the immune system towards a tolerogenic state, resulting in chronic low-grade inflammation (LGI) that is commonly present in obesity, poor nutrition, exposer to chemicals or allergens, prodromal stage of various illnesses, and chronic diseases. KYN, kynurenic acid, xanthurenic acid, and cinnabarinic acid are aryl hydrocarbon receptor ligands that serve as immunomodulators. Furthermore, TRP-KYN pathway enzymes are known to be activated by the stress hormone cortisol and inflammatory cytokines, and genotypic variants were observed to contribute to inflammation and thus various diseases. The tryptophan 2,3-dioxygenase, the indoleamine 2, 3-oxygenases, and the kynurenine-3-monooxygenase are main enzymes in the pathway. This review article discusses the TRP-KYN pathway with special emphasis on its interaction with the immune system and the tolerogenic shift towards chronic LGI and overviews the major symptoms, pro- and anti-inflammatory cytokines, and toxic and protective KYNs to explore the linkage between chronic LGI, KYNs, and major psychiatric, including depressive disorder, bipolar disorder, substance use disorder, post-traumatic stress disorder, schizophrenia, and autism spectrum disorder.

Since the early 1980s, a large number of research works on enzymes from the red king crab hepatopancreas have been conducted. These studies have been relevant both from a fundamental point of view for studying the enzymes of marine organisms and in terms of the rational management of nature to obtain new and valuable products from the processing of crab fishing waste. Most of these works were performed by Russian scientists due to the area and amount of waste of red king crab processing in Russia (or the Soviet Union). However, the close phylogenetic kinship and the similar ecological niches of commercial crab species and the production scale of the catch provide the bases for the successful transfer of experience in the processing of red king crab hepatopancreas to other commercial crab species mined worldwide. This review describes the value of recycled commercial crab species, discusses processing problems, and suggests possible solutions to these problems. The main emphasis is placed on the enzymes of the hepatopancreas as the most highly salubrious product of waste processed from red king crab fishing.

We propose that the short−term memory (STM), when processing a sentence, uses two uncorrelated processing units in series. The clues for conjecturing this model emerge from studying many novels of the Italian and English Literatures. This simple model, referring to the surface of language, seems to describe mathematically the input-output characteristics of a complex mental process involved in a reading/writing a sentence. We show that there are no significant mathematical/statistical differences between the two literary corpora by considering deep-language variables and linguistic communication channels, therefore, the surface mathematical structure of alphabetical languages is very deeply rooted in human mind, independently of the language used. The first processing unit is linked to the number of words between two contiguous interpunctions, variable Ip, approximately ranging in Miller’s 7±2 range; the second unit is linked to the number of Ip’s contained in a sentence, variable MF, ranging approximately from 1 to 6. The overall capacity required to process fully of a sentence ranges from 8.3 to 61.23 words, values that can be converted into time by assuming a reading speed, giving the range 2.64~19.54 seconds for fast-reading and 5.3~30.1 seconds for average reader. Since a sentence conveys meaning, the surface features we have found might be the starting point to arrive at an Information Theory that includes meaning.

Rett syndrome (RTT) is mainly caused by mutations in methyl CpG-binding protein 2, cyclin-dependent kinase-like 5, or forkhead box protein G1. These RTT-causing proteins harbor an intrinsically disordered region (IDR) whose conformation exhibits spatiotemporal heterogeneity, which not only confer versatility to the protein, but also implicates them in diseases. The IDR generally evolves more rapidly than an ordered structure. In this study, we examined the relationship between pathogenic RTT-associated point mutations in RTT-causing proteins and the evolutionary dynamics of sequence features including structural order–disorder, phosphorylation sites, and evolutionary rates. We also analyzed the molecular properties and evolution of proteins that interact with RTT-causing proteins in terms of phylogenetic profiles, tissue specificity, subcellular localization, expression level, and functions. The results indicate that constrained IDRs may function by forming contacts with other regions in the protein sequence causing pathogenic missense mutations likely to arise in the rapidly evolving IDR and affect molecular networks, leading to disease. The results also provide novel insights into the genetic basis for RTT and the evolution of the neocortex in higher vertebrates.

This proposal advocates using energy states as fundamental units for quantum computer programming. Each programming unit is conceptualized as an energy state, manifested as an essence-unit. The essence-unit, defined as the minimal form encapsulating complete uniqueness or specificity, serves as the cornerstone of this paradigm. This approach allows recording various forms of matter on quantum computers in essence-units representing their energy states. Recording energy states is achieved through the creation of four distinct coherent potentials facilitated by quantum dots or crystals. It is crucial to note that these energy states, embodied in essence-units, cannot be subdivided. The study examines the intricate relationship between similarity, fractals, and uniqueness in quantum dot operations, elucidating their profound implications for information transfer efficiency. Normalized entropy quantifies the charge localization in quantum dot impurities in systems exhibiting distortions. Utilizing N-level recording and entropy-fractal dimension equivalence enables this paper to elucidate the potential of quantum dots in reducing transmission time and modeling complex systems. The proposed methodology represents a significant departure from the current state of quantum computing, offering unprecedented potential for overcoming previously intractable challenges.

In the dynamic field of mental health care, the nuanced application of Artificial Intelligence (AI) through Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) is a pivotal innovation in patient empowerment and service optimization. This study introduces a distinctive online therapy platform that capitalizes on the synergy of NLP and ASR to offer unprecedented levels of interactive and personalized therapeutic interventions. The architecture of our system is meticulously detailed, featuring an ASR component with an impressive Word Error Rate (WER) of 14% when trained on the diverse French subsets of the Mozilla Common Voice dataset, complemented by a high-precision NLP framework skilled in processing and responding to user inputs. The evaluation of our system highlights its efficacy in enhancing therapy sessions and user satisfaction, with an emphasis on the qualitative aspects of user feedback. The paper addresses challenges such as dataset representativeness and language model refinement and articulates the strategic solutions employed to overcome them. The paper concludes with forward-looking perspectives on AI's role in mental health services, advocating for the creation of sophisticated, language-specific datasets and models to satisfy the increasing demands of online therapy, reflecting a growing commitment of patients in the management of their therapy. This research underscores the transformative impact of AI in advancing mental health care into the digital age, representing a significant evolution over existing methodologies.

We present detailed models at the hydrodynamics and decoherence phenomena observed in ideal quantum gases. The model is expressed in terms of a quantum field theory, where the action involves both hydrodynamic and decoherence terms. The density-current relation is derived, and the current Green function is introduced. The effective action for two fields is presented, and the physical field is defined in terms of the bare action. Identities such as CTP symmetry, transposition, and the Ward identity are discussed. The Green function and its inverse for different components are also explored. These notes provide a comprehensive understanding of the theoretical framework for studying the hydrodynamics and decoherence of ideal quantum gases.