Clothing consumers mostly purchase, use, and dispose of clothing products in a linear business model. This approach pollutes the environment and causes economic and social crises in the ecosystems. This study evaluates the amount of generated postconsumer clothing waste (PCCW) and proposes recycling as a sustainable waste management approach. Data were collected to understand disposal behavior of consumers with 474 respondents selected from households in Bahir Dar city. The findings show that all the determinant factors on the PCCW generation level except religion were significant. The most frequently disposed type of clothing waste is jeans-trouser (51%). The CEBM was used to develop alternative products from a jeans-based clothing waste. The jean's fiber and fabric-reinforced composite materials were developed using unsaturated polyester resin as a sustainable way of PCCW management strategy. The shredded form fiber-reinforced composite was found to have a smoother surface than that of the chopped or cut fabric-reinforced composite. The product was characterized by physical and mechanical properties. The study can be further extended to the scope of the entire consumers in Ethiopia and alternative recyclable products can be studied.

Obesity entails metabolic alterations across multiple organs, highlighting the role of inter-organ communication in its pathogenesis. Extracellular vesicles (EVs) are communication agents in physiological and pathological conditions and although they have been associated with obesi-ty-comorbidities, their protein cargo in this context remain largely unknown. To decipher the messages encapsulated in EVs, we isolated plasma derived EVs from diet induced obese murine models. Obese plasma EVs exhibited a decline in protein diversity while control EVs revealed significant enrichment in protein folding functions, highlighting the importance of proper folder in maintaining metabolic homeostasis. Previously, we revealed that gut derived EVs proteome holds particular significance in obesity. Here, we compared plasma and gut EVs and identified four proteins exclusively present in the control state of both EVs, revealing the potential for a non-invasive assessment of gut health by analyzing blood derived EVs. Given the relevance of post-translational modifications (PTMs), we observed a shift in chromatin-related proteins from glycation to acetylation in obese gut EVs, suggesting a regulatory mechanism targeting DNA transcription during obesity. This study provides valuable insights into novel roles of EVs and protein post-translational modifications in the intricate mechanisms underlying obesity, shed-ding light on potential biomarkers and pathways for future research.

Mechanical properties of three-dimensional (3D) printed resin can be improved by decreasing oxygen concentrations during post-curing, and nitrogen saturated post-curing has been applied for the purpose. This study aimed to evaluate and compare the color stability of 3D printed resin crowns post-cured in normal air and nitrogen saturated conditions. Crowns were fabricated with 3D printer and post-cured in normal air (control group; air) or nitrogen saturated condition (experimental group; nitrogen). The specimens in each group were subdivided into four subgroups, each exposed to different discoloration agents: distilled water, coffee, wine, and curry. Post-immersion color changes were measured using a digital spectrophotometer and analyzed through repeated measures ANOVA. Fourier Transform Infrared (FT-IR) spectroscopy evaluated the degree of conversion of resin over immersion times for both post-curing conditions. Comparing the effects of post-curing conditions, significant difference was found between control and experimental groups over immersion time in wine and curry subgroups. In FT-IR analysis, there was significant difference in degree of conversion between the air and nitrogen groups from 10 to 300 seconds. The findings suggest that nitrogen saturated post-curing can potentially enhances the conversion rate of 3D printed resin crowns, thereby improving their color stability.

In the evolving landscape of higher education, St. Michael's College (SMC) recognizes the significant impact of the COVID-19 pandemic on student welfare. This paper emphasizes the critical importance of addressing mental health, time management, and stress in the college environment, underlining their influence on academic success and personal growth. The call to action urges students to prioritize their well-being. Diverse cultural and religious perspectives, including Islam, emphasize the importance of a balanced mind and body. The wide-ranging literature review uncovers empirically supported strategies for mental health, time management, and stress reduction. Personal autonomy emerges as crucial in addressing these challenges. This study underscores the importance of these dimensions, calls for student action, and highlights the diversity of perspectives contributing to holistic well-being.

In the aftermath of the COVID pandemic, post-COVID-19 syndrome (PCS) remains a challenge and may continue to represent a major health problem in the future. Moreover, the influence of type 2 diabetes and being overweight on PCS remains unclear. This study aimed to assess this influence. We performed an observational study from October 2020 to July 2022, which included 466 patients (269 males and 197 females) with a median age of 65. They were hospitalized due to COVID-19 pneumonia and had persistent symptoms after 1 month of COVID-19 infection. The patients were divided into four groups according to the study objectives: patients with type 2 diabetes, overweight patients, overweight patients with type 2 diabetes, and normal-weight patients without type 2 diabetes. The clinical and demographic data collected during hospitalization and regular visits at the Community Healthcare Center dr. Adolf Drolc Maribor were analyzed. Our results showed that type 2 diabetes patients had a more difficult course of treatment and longer hospitalization. Moreover, more type 2 diabetes patients underwent rehabilitation than the other study groups. The prevailing symptoms in our patients with PCS were dyspnea and fatigue, mostly in female patients with type 2 diabetes. Our study also showed that more women with type 2 diabetes and more overweight women with type 2 diabetes suffered from secondary infections. Further, more overweight patients were treated in the intensive care unit compared to the other groups. However, our study showed an interesting result that patients with type 2 diabetes had the shortest PCS durations. Type 2 diabetes and being overweight are risk factors for PCS onset and prolonged duration. Therefore, our data that revealed a shorter duration of PCS in type 2 diabetes patients compared to the other investigated groups was unexpected. We believe that answering this question regarding the results will enable us to improve PCS treatment in general.

This study delves into the dynamic intersection of artificial intelligence (AI) and English Language Teaching (ELT) within the post-pandemic educational landscape. Grounded in Constructivism, Connectivism, and Technological Pedagogical Content Knowledge (TPACK), this research explores how AI integration impacts language instruction efficiency, benefits and challenges perceived by St. Michael's College students, differences in learning outcomes, and its potential to address the digital divide. Employing a qualitative approach, the study employs a rigorous method that includes intensive in-depth reviews of literature, maximum purposeful sampling, informal interviews, observation, and thematic analysis. Through these methods, data was collected from 30 participants, leading to the identification of five key themes. Findings suggest that AI enhances instructional efficiency, offers personalized learning experiences, and bridges gaps in remote settings. However, challenges and the importance of pedagogical balance emerged. The study underscores the need for adaptive pedagogical strategies to fully harness AI's potential. Future directions and limitations are discussed, emphasizing the study's implications for shaping the future of ELT.

With the intensification of population aging in rural areas, it becomes increasingly important to analyze the post-retirement migration intention of rural residents and the variables influencing these intentions. In this study, we focus on rural residents aged 45 to 60 and investigate the main variables that influence the post-retirement migration intention of rural residents, using survey data collected from 164 households in three different rural areas and the logistic regression model. From the result of this study, we found that gender, part-time employment, savings level, children's residence and occupation stability, and interest in urban living positively affect migration intention. In contrast, the number of rural companions, relationships with others in rural areas, and evaluation of rural living have a negative effect. In addition, we employ age and the proportion of mobile income as control variables to examine the variables that influence the post-retirement migration intention in different age groups and mobile income groups. The analysis reveals that, the variables influencing post-retirement migration intention varied across age groups and mobile income groups, and this variation can be attributed to the differences between groups' characteristics.

Amidst the transformative backdrop of the post-Covid-19 context, the researcher embarks on a qualitative exploratory study to investigate the intricate interplay of religiosity, educational engagement, and campus life among college students at St. Michael's College, Iligan City. Guided by a theoretical framework integrating Vygotsky's sociocultural theory, Fredricks et al.'s engagement theory, and Pascarella & Terenzini's educational impact theory, this study delves into the dynamic fruition of religiosity in response to the pandemic's impact. Employing purposeful and maximum variation sampling, the investigation unravels narratives from interviews, focus group discussions, and observations. Through thematic analysis, themes of "Adaptive Integration," "Religious Resilience," "Community Nexus," "Challenges of Synchronization," and "Identity Negotiation" emerge. In the discourse of these themes, strategies students employ to navigate challenges arising from this confluence are unearthed. Rooted in a deep understanding of the context, this study presents implications for fostering holistic growth within an inclusive educational environment. As the findings and insights are presented, stakeholders are invited to reimagine the role of religiosity, educational engagement, and campus life in shaping the holistic student experience in a world forever changed.

Long Covid is a term used for patients who have recovered from COVID-19 but exhibit persistent cognitive dysfunction, including mental confusion, difficulties in attention, impairment in executive functions and slow movements, among other common symptoms. A study was conducted with 65 patients who had a positive RT-PCR diagnosis and reported symptoms of cognitive impairment, such as memory loss and attention difficulties after recovery. The patients underwent neuropsychological evaluation and completed questionnaires on cognition, mood, and quality of life. During the cognitive screening, 71% of the patients showed alterations, with deficits in visual memory (69%), language (54%), visuospatial construction (49%), verbal episodic memory (37%), executive functions (36%), attentional abilities (34%), and premorbid intelligence (12%). It is important to highlight the need for treatments and further studies to understand the long-term side effects of this disease.

Our research aims to address the urgent need for retrofitting measures to enhance the resilience of damaged coupling beams in real-life scenarios. We conducted a series of tests on scaled-down prototype coupling beams, carefully adhering to similarity theory requirements to ensure accurate representation. The test results showed that the chord rotation capacities of different specimens with the same diagonal bundle varied significantly due to the difference in the confinement levels. The specimens with lower level of confinement were found to have less displacement capacity. When the specimens were strengthened by wrapping with GFRP after damage, it was observed that the increase in confinement level significantly increased the chord rotation capacities of all specimens. In addition, in the tests before the strengthening, the elongation behavior of the specimens is observed until the damage development reaches approximately 2% chord rotation. With the in-crease in chord rotation, the elongation behavior of the specimens stops with increasing damage and then shortening behavior is observed. After retrofitting, it was observed that the axial elongation continued in a limited manner depending on the location and size of the concrete separated from the beam core in the case of collapse before strengthening, and then the beam showed a shortening behavior predominantly. After strengthening studies, our findings contribute valuable insights into the behavior and performance of damaged coupling beams, emphasizing the potential of urgent GFRP wrapping as a viable solution to enhance the seismic resilience of middle- and high-class buildings with structural walls after seismic events.

Phosphocalcium metabolism disorders have emerged as potential complications in individuals recovering from COVID-19. This abstract provides a concise summary of the current understanding of phosphocalcium metabolism disturbances post-COVID, their prevalence, clinical manifestations, and underlying mechanisms. The impact of COVID-19 on phosphorus, calcium, and parathyroid hormone levels is discussed, highlighting the importance of regular monitoring and appropriate management strategies. The abstract emphasizes the need for further research to enhance our understanding of these disorders and improve post-COVID patient care.

Fasting, a practice with historical roots in various cultures, has recently garnered significant interest in the field of medicine. In this article, we delve into the mechanisms underlying fasting-induced autophagy and its therapeutic applications for spike protein associated pathology. We explore the therapeutic potential of fasting on spike protein-related pathology and the role of interventions to upregulate autophagy, including compounds like spermidine, resveratrol, rapamycin, and metformin. In conclusion, fasting, coupled with an understanding of its nuances, holds promise as a therapeutic intervention for spike protein related diseases; with broad implications for human health. This review presents the therapeutic possibility of using autophagy to treat spike protein related diseases, and details the interventions to deploy this therapeutic modality.

Post-translational modifications (PTMs) represent a cornerstone in the complexity of the proteome, significantly contributing to diversifying protein structure and function. PTMs can significantly influence protein function, stability, localization, and interactions with other molecules. Therefore, it is important when choosing a protein expression system to ensure the precise incorporation of PTMs during protein synthesis, which is paramount for producing biologically active proteins. Cell-free protein synthesis (CFPS) system has emerged as a powerful protein synthesis platform and research toolkit in synthetic biology. The open nature of the system allows the reaction environment to be tailored to any protein of interest to promote specific PTMs, thus allowing for the production of a protein with the desired modifications. This review presents various PTMs achieved in the CFPS systems, providing insights into the current challenges, successes, and future prospects.

Fasting, a practice with historical roots in various cultures, has recently garnered significant interest in the field of medicine. In this article, we delve into the mechanisms underlying fasting-induced autophagy and its therapeutic applications for spike protein associated pathology. We explore the therapeutic potential of fasting on spike protein-related pathology. Additionally, we discuss factors that affect fasting, such as duration, type (dry vs. water), and the role of specific compounds like spermidine, resveratrol, rapamycin, and metformin. Furthermore, we analyse the interactions between fasting and other practices such as exercise, and highlight important considerations regarding participant characteristics, including pregnancy, breastfeeding, medication interactions, and metabolic disorders. In conclusion, fasting, coupled with an understanding of its nuances, holds promise as a therapeutic intervention with broad implications for human health.

Vitamin B9(folate)/B12 deficiency is known to induce brain structural and/or functional retardations. Besides neural tube defects in case of major consequences, several mild deregulations also lead to deleterious effect after birth, and folate supplementation recommended in some countries usually stop at the end of the first trimester. Various hormonal receptors were shown to be deregulated in brain tissue under this context. The glucocorticoid receptor (GR) is particularly sensitive to epigenetic regulation and post-traductional modifications. In a mother-offspring rat model of vitamin B9/B12 deficiency, we investigated whether a prolonged folate supplementation could restore the GR signalization in the hypothalamus. Our data showed that a deficiency in folate and vitamin B12 during the in-utero and the early postnatal periods was associated with reduced GR expression inn the hypothalamus. We also described for the first time a new post-traductional modification of GR that impaired ligand binding and GR activation, leading to decreased the expression of one of the GR targets in the hypothalamus, AgRP. Moreover, this brain-impaired GR pathway was associated with behavioral perturbations during offspring growth. Importantly, perinatal and postnatal supplementation with folic acid helped restore GR mRNA level and activity in hypothalamus cells and improved behavioral deficits.

Abstract: Background: Stigma in health can result in a broad range of vulnerabilities and risk for patients and healthcare providers. The media plays a role in people’s understanding of health and stigma is socially constructed through many communication channels including via media framing. Among health issues that were affected by stigma recently were the Monkeypox and Covid-19. Objectives: This research aims to examine how The Washington Post framed stigma around mon-keypox and COVID-19. Guided by the framing theory and stigma theory, online news coverage for monkeypox and COVID-19 were analyzed to understand the construction of social stigma through the media reporting. Methods: This research employed a qualitative content analysis to compare news framing in The Washington Post online news regarding monkeypox and COVID-19. Results: Based on endemic, reassurance, and sexual transmission frames, the The Washington Post predominantly defined Africa as the source of the disease, blames gay communities, and empha-sizes no need to worry about the spread of the monkeypox virus. For the COVID-19 coverage, The Washington Post described China as the source of the coronavirus and constructs the image of panic towards the spread of the virus. Conclusions: The shifts in stigma discourse essentially manifest racism, xenophobia, and sexism in public health. This research affirms that the media reinforces stigma phenomenon in health through framing and offers constructive suggestions for mitigating this issue.

During the response of plants to adverse stresses, aquaporin (AQP) plays a prominent role in membrane water transport based on received upstream signals. In addition, they have various physical parts for dealing with environmental stresses. Due to the importance of the AQP gene family, studies have been conducted investigating the function and regulatory system of these genes. However, many of their molecular aspects are still unknown. This study aims to carry out a genomic-wide investigation of the AQP gene family in durum wheat using bioinformatics tools and to investigate the expression patterns of some members in response to salt stress. Our results showed that there are 80 TtAQP genes in durum wheat, which are classified into four main groups based on phylogenetic analysis. Many duplications were observed between the members of the TtAQP gene family, and high diversity in response to post-translational modifications was observed between TtAQP family members. The expression pattern of TtAQP genes disclosed that these genes are primarily upregulated in response to salt stress. Besides, qPCR data revealed that TtAQPs are more induced in delayed responses to salinity stress. Overall, our findings illustrate that TtAQP members are diverse in terms of their structure, regulatory systems and expression levels.

An adaptor signature can be viewed as a signature concealed with a secret value and, by design, any two of the trio yield the other. In a multiparty setting, an initial adaptor signature allows each party create additional adaptor signatures without the original secret. Adaptor signatures help address scalability and interoperabity issues in blockchain. They can also bring some important advantages to cryptocurrencies, such as low on-chain cost, improved transaction fungibility, and less limitations of a blockchain’s scripting language. In this paper, we propose a new two-party adaptor signature scheme that relies on quantum-safe hard problems in coding theory. The proposed scheme uses a hash-and-sign code-based signature scheme introduced by Debris-Alazard et al. and a code-based hard relation defined from the well-known syndrome decoding problem. To achieve all the basic properties of adaptor signatures formalized by Aumayr et al., we introduce further modifications to the aforementioned signature scheme. We also give a security analysis of our scheme and its application to the atomic swap. After providing a set of parameters for our scheme, we show that it has the smallest pre-signature size compared to existing post-quantum adaptor signatures.

Deep Learning is developing interesting tools which are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green-AI literature, we here propose a shallow neural network to perform an efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results obtained on images from the training set and on unseen images, using both the non-expensive network and the widely used very deep ResUNet show that the proposed network computes images of comparable or higher quality in about one fourth of time.

Total 219 countries and territories globally suffering from the recent pandemic COVID-19 is now in its second wave with more brutality, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . It has several symptoms like as persistent fever; respiratory illnesses; cough; fatigue; shortness of breath; loss of appetite; persistent pain or pressure in the chest; dysgeusia; acute respiratory distress syndrome (ARDS) etc., and here the things to worry about is the development of pulmonary fibrosis after COVID-19 in both peoples who had died of due to acute respiratory distress syndrome (ARDS) or those who survived. Due to COVID-19, dysregulated immune response and wound repair mainly in elderly patients causes this secondary pulmonary fibrosis. Thus using anti-fibrotic agents could be meaningful in these circumstances although their efficacy in treating COVID-19 is subject to more detailed laboratory research works. In this review article you will get to know about the lung fibrosis generation due to COVID-19 infection, about anti-fibrotic agents and the currents challenges of this field.

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or in transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing imposes a major threat on the modern cryptosystems based on modular arithmetic whereby, even the computationally hard problems which constitute for the strength of the modular arithmetic ciphers could be solved in deterministic time. This threat triggered post-quantum cryptography research in order to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides a review of the various post-quantum cryptography and, in specific, code-based cryptography research dimensions. The research directions that are yet to be explored in code-based cryptography research is another key contribution of this paper.

In this paper, we introduce a computational procedure that enables autonomous LEO laser trackers endowed with INSs to increase the current accuracy when shooting at middle distant medium-size LEO debris targets. The code is designed for the trackers to throw the targets into the Atmosphere by means of ablations. In case that the targets are eclipsed to the trackers by the Earth, the motions of the trackers and targets are modeled by equations that contain post-Newtonian terms accounting for the curvature of space. Otherwise, when the approaching targets become visible for the trackers, we additionally use more accurate equations, which allow to account for the local bending of the laser beams aimed at the targets. We observe that under certain circumstances the correct shooting configurations that allow to safely and efficiently shoot down the targets, differ from the current estimations by distances that may be larger than the size of many targets. In short, this procedure enables to estimate the optimal shooting instants for any middle distant medium-size LEO debris target.

This study investigated the prevalence and severity of depression, anxiety, and stress and determined the association between various factors, social support, and depression and anxiety among university healthcare workers in Malaysia after the government lifted the movement control order (MCO) put in place to curb the coronavirus disease 2019 (COVID-19) pandemic. This online, cross-sectional survey recruited 399 participants from two university hospitals, and they were administered a self-reported questionnaire on demographic, personal, and clinical characteristics; COVID-19-related stressors; and coping. In addition, they completed the Multidimensional Scale of Perceived Social Support (MSPSS) to measure perceived social support, as well as the 21-item Depression, Anxiety, and Stress Scale (DASS-21) to assess depression, anxiety, and stress. We found that the prevalence of depression, anxiety, and stress were 21.8%, 31.6%, and 29.1%, respectively. Participants with moderate to extremely severe depression, anxiety, and stress made up 13.3%, 25.8%, and 8.1% of the sample, respectively. Being single or divorced, fear of frequent exposure to COVID-19 patients, those who agreed that their area of living had a high prevalence of COVID-19 cases, and uncertainty regarding the prevalence of COVID-19 cases in the area of living were associated with higher odds of depression and anxiety. Conversely, having more than three children and greater perceived friend support were associated with lower odds of depression and anxiety. The prevalence of depression, anxiety, and stress remained elevated even after the MCO was lifted.

The “tubulin code” combines different α/β-tubulin isotypes with several post-translational modifications (PTMs) to generate microtubule diversity in cells. During cell division, specific microtubule populations in the mitotic spindle are differentially modified, but only recently has the functional significance of these modifications started to be elucidated. In particular, α-tubulin detyrosination of stable microtubules in the spindle was shown to guide chromosomes during congression to the metaphase plate and allow the discrimination of mitotic errors, whose correction is required to prevent chromosomal instability (CIN), a hallmark of human cancers. Although alterations in certain tubulin PTMs have been reported in human cancers, it remains unclear whether and how tubulin PTMs have any functional implications for cancer cell properties. Here we review the role of the tubulin code in chromosome segregation during mitosis, together with the emerging cancer tubulin code and discuss possible links, as well as the respective diagnostic, prognostic and therapeutic implications for human cancers.

This conceptual article is aimed at evaluating the COVID19 impacts on the global economy and to postulating an ‘economic paradigm shift’. We argue that the existing economic, political, and trade principles have been challenged due to the global pandemic issue. We also compare the COVID19 impact on Western countries and Eastern countries with their control mechanisms. The emerging of new economies is due to the travel restrictions and disrupt of the global economies are discussed that pave a gateway to the economic paradigm shift. The article finally assesses the advent of 5G technology and its implications to face any future health threats that will lead to the next economic paradigm shift in the modern world.

Since January, the disease caused by the Sars Cov-2 virus has spread and following pandemic. In a few months, the virus is seriously affecting the health systems of the various countries of the world and placing people in difficult psychological conditions. Few scientific resources on mental health have been published but still no one has proposed an action plan to cope the future psychological problems.This manuscript provides a plan to easily frame the priorities of mental health area related to COVID-19 to be taken into consideration which should be disseminated and that should be known by all health professionals and also by the major administrators of public health.

The incidence of cancer in organ transplant patients is higher than in the non-transplanted population. The incidence increases with increasing age. The use of Fludeoxyglucose positron emission tomography/CT (PET/CT) is sensitive and specific to detect PTLD LD compared with conventional CT imaging. We analyzed the medical data of 127 patients aged over 60 years, who underwent heart transplantation in the above period, who have been practicing early CT-PET for diagnostic purposes between February 2007 and October 2018. Of 127 consecutive patients who underwent CT-PET, SUVs up than 4, were found in 84 patients of which 20 were affected by PTLDs, seven patients were affected by chronic non neoplastic inflammatory diseases, the remainder were affected by other neoplasms. The favorable effect of physical activity programs on cardiorespiratory and psychomotor function occur in all patients. In conclusion, CT-PET at the first doubts about the possibility of the development of neoplasms has allowed a diagnosis and then a treatment more quickly. Education in behavioral norms that improve the patient's quality of life is necessary.

The maintenance of high quality standards for prolonging shelf life of fruit and maintaining sensory and nutritional quality is a priority the horticultural products. The aim of this work was to test the effectiveness of a single treatment of edible coating based on Aloe arborescens and a combined treatment of 1-Methylcycyclopropene and edible coating to prolong the shelf-life of "Settembrina" white flesh peach fruit. White flesh peach fruit were harvested at the commercial ripening stage and treated with edible coating (EC) or 1-MCP +EC and stored for 28 days at 1°C. After 7, 14, 21 and 28 days fruits were removed from cold storage, transferred at 20°C and then analyzed immediately (cold out) and after 6 days (shelf life) to evaluate the combined effect of cold storage and room temperature. Weight loss, physical, chemical and sensory parameters were measured. Fruit treated with EC and 1-MCP +EC kept their marketing values better than CTR after 14 days of storage and 6 days of simulated shelf life, in terms of flesh firmness, total soluble solids and titratable acidity as well as sensory parameters. After 21 days of storage, all treatments showed a deterioration of the quality parameters. The single and combined application of Aloe-based coating (with 1-MCP) slowed down the maturation processes of the fruit, limited the weight loss and preserved its organoleptic characteristics.

Background: Many randomized controlled trials (RCTs) and systematic reviews (SRs) on acupuncture treatment for post-acute stroke dysphagia have been published. Due to conflicting results an overview of SRs to summarize and assess the quality of this evidence to determine whether acupuncture is effective for this disease was conducted. Methods: Seven databases were searched for SRs and/or Meta-analysis of RCTs and quasi-RCTs on acupuncture for post-acute stroke dysphagia. Two authors independently identified SRs and meta-analyses, collected data to assess the quality of included SRs and meta analyses according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the revised Assessment of Multiple Systematic Reviews (AMSTAR 2). Results: 31 SRs were identified. Quality of 22 SRs was critically low, 5 SRs were low, and 4 Cochrane SRs were moderate when evaluated by AMSTAR2. 17 SRs reported 85.2-96.3% items of PRISMA. Five SRs included explanatory RCTs, 16 SRs included pragmatic RCTs, and 10 SRs included both. Conclusion: Currently evidence on the effectiveness of acupuncture on post-acute stroke dysphagia is low quality. Type of study appeared to have no direct influence on the result, but the primary outcome measures showed a relationship with the quality of SRs. High quality trials with large sample sizes should be the focus of future research. PROSPERO REGISTRATION NUMBER: CRD42019134163

Understanding the role of risk regions identified by genome-wide association studies (GWAS) have made considerable progress lately referred to the post-GWAS era. Annotation of the genes to the GWAS and fine-mapped functional variants, and understanding their biological pathway/gene networks enrichments is expected to give rich dividend by elucidating the mechanisms underlying prostate cancer. To this aim, we compiled and analysed currently available post-GWAS data on genes identified through GWAS and validated through experimental studies in prostate cancer to investigate molecular biological pathways enriched for assigned functional genes. The results highlight some well-known cancer signalling pathways, antigen presentation process and enrichment in cell growth and development gene networks suggesting prostate cancer may result from the accumulation of the effects of functional variants through multiple gene sets and pathways. The upstream analysis identifies critical transcription factors, which supplements the results regarding the regulatory role of the post-GWAS genes. We also identified the common genes between post-GWAS and three well-annotated prostate cancer Oncomine data in patient samples in order to uncover possible main genes in prostate cancer development/progression. Post-GWAS generated knowledge of gene networks and pathways, if analysed further and targeted appropriately, will have an important impact on clinical management of the disease.

The type of tail of sheep is an important economic trait. However, the candidate genes associated with the tail type are uncertain. The objective of this study was to identify the genetic region and genotype responsible for the tail type phenotype. Here we perform a genome-wide association study (GWAS) in 40 large tailed Han sheep and 40 Altay sheep as case and 40 Tibetan sheep as control. The results indicated that a total 31 genome-wide significant SNPs associated with type of tail traits were detected. For significant SNPS loci, determine its physical location, and screening of candidate genes within section. By combining information of previously reported and annotated biological functional genes, we identified SPAG17, Tbx15, VRTN, NPC2, BMP2 and PDGFD as the most promising candidate genes for type of tail traits. Based on the above identified candidate genes on type of tail traits, we selected BMP2 and PDGFD to conduct the genetic effect analysis in a large Altay sheep and Tibetan sheep population. Rs119 T>C in the exon1 of BMP2 gene and 1 SNPs in the exon4 (rs69 C>A) of PDGFD gene were detected, rs119 that located on exon1 of BMP2 gene was TT genotype in Altay sheep, while with CC genotype in Tibetan sheep. On rs69 of PDGFD gene, Altay sheep with CC genotype, however, Tibetan sheep with AA genotype. These results indicated that the significant associations of SNPs detected in GWAS were indirectly caused by the genetic effects of BMP2 and PDGFD on sheep tail fat deposition.

Several researchers have contemplated deep learning-based post-filters to increase the quality of statistical parametric speech synthesis, which perform a mapping of the synthetic speech to the natural speech, considering the different parameters separately and trying to reduce the gap between them. The Long Short-term Memory (LSTM) Neural Networks have been applied successfully in this purpose, but there are still many aspects to improve in the results and in the process itself. In this paper, we introduce a new pre-training approach for the LSTM, with the objective of enhancing the quality of the synthesized speech, particularly in the spectrum, in a more efficient manner. Our approach begins with an auto-associative training of one LSTM network, which is used as an initialization for the post-filters. We show the advantages of this initialization for the enhancing of the Mel-Frequency Cepstral parameters of synthetic speech. Results show that the initialization succeeds in achieving better results in enhancing the statistical parametric speech spectrum in most cases when compared to the common random initialization approach of the networks.

We aimed to evaluate the feasibility and preliminary efficacy of trauma-focused group psychotherapy in adolescents who experienced traumatic events in Korea. Participants were assigned and recruited from two sites in Korea. Children in Disaster: Evaluation and Recovery (CIDER) V1.0 is a trauma-focused group psychotherapy approach consisting of psychoeducation, normalization, stabilization, and techniques of managing the traumatic memory. The CIDER intervention consists of eight 50-minute-long sessions. The effectiveness of the intervention was evaluated using the Korean version of the Children’s Response to Traumatic Events Scale-Revised (K-CRTES-R), the Beck Depression Inventory (BDI), the State Anxiety Inventory for Children (SAIC), and the Pediatric Quality of Life Inventory (PedQL). Data were analyzed by Wilcoxon signed-rank test. We recruited 22 traumatized adolescents (mean age 16 years; SD 1.43; range 13–18 years old; 71.4% boys) in this pilot study. The K-CRTES-R scores were significantly improved (Z = −2.85, p < 0.01). The BDI demonstrated the effectiveness of the therapy (Z = −2.35, p < 0.05). The assessment of the PedQL supported the effect of CIDER (Z = −3.08, p < 0.01). However, there was no statistically significant differences in the SAIC scores (Z = −1.90, p > 0.05). The results show that there is preliminary evidence that CIDER intervention reduces post-traumatic stress and depressive symptoms and improves quality of life. Our findings indicate that CIDER is feasible for treating adolescents exposed to traumatic events. Larger controlled trials are needed to establish the efficacy of this trauma-focused group psychotherapy and examine its impact on post-traumatic stress disorder.

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6,497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain the text-based risk score algorithm as predictive of death within 30 days of surgery. We studied the additional performance obtained by including text-based risk score as a predictor of death along with other structured data based clinical risk factors. The C-statistic of a logistic regression model with 5-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.

In this paper the development process of a deployable modular sandwich panelized system for rapid assembly building construction is presented, and its structural performance under some different action effects is investigated. This system, which includes an innovative sandwich panel and its integrated connections, can be used as structural walls and floors in quickly assembled post-disaster housing, as well as load bearing panels for pre-fabricated modular construction and semi-permanent buildings. Panels and connections are composed of a pneumatic fabric formwork, and two 3-D high-density polyethylene (HDPE) sheets as the skins, filled with high-density rigid Polyurethane (PU) foam as the core. HDPE sheets manufactured with a studded surface considerably enhance the stress distribution, buckling performance and delamination strength of the sandwich panel under various loading conditions. The load-carrying behaviour of the system in accordance with some ASTM standards is presented here. The results show the system satisfies the codes criteria regarding semi-permanent housing.

Building a social security system to ensure Singapore residents have peace of mind in funding for retirement has been at the top of Singapore Government’s policy agenda over the last decade. Implementation of the Lifelong Income For the Elderly (LIFE) scheme in 2009 clearly shows that the government spares no effort in improving its pension scheme to boost its residents’ income after retirement. Despite the recent modifications to the LIFE scheme, Singapore residents must still choose between two plans: the Standard and Basic plans. To enhance the flexibility of the LIFE scheme while maintaining its simplicity, we propose some plan modifications such that scheme members do not face a dichotomy of plan choices. Instead, they select two age parameters: the Payout Age and the Life-annuity Age. This paper provides an actuarial framework for determining members’ payouts and bequests based on the proposed age parameters. We analyze the net cash receipts and internal rate of return (IRR) for various plan-parameter configurations. This information helps members make their plan choices. To address cost-of-living increases we propose to extend the plan to accommodate an annual step-up of monthly payouts. By deferring the Payout Age from 65 to 68, members can enjoy an annual increase of about 2% of the payouts for the same first-year monthly benefits.

The authors argue that despite significant numbers of foreign workers present in the Russian labor market Russia remains unattractive for highly skilled foreign workers. The economic crisis the country has faced since 2014 has resulted in a further outflow of foreign nationals from OECD countries. So Russia has to look for employees among newcomers from the former Soviet states, the majority of whom come from Central Asia. Russian politics with regard to immigration is characterized by the collision between “geopolitical” and domestic policy rationales. On one hand, seeking to maximize its influence in the post-Soviet space Russia provides preferential conditions of employment for citizens of partner countries in the framework of the Eurasian Economic Union. On the other hand, the Kremlin is afraid to liberalize its approach to immigration and the integration of immigrants through fear of losing popularity among the electorate. The second substantial feature of Russian immigration politics is an extreme level of securitization of migration issues. The government considers these issues primarily through a policing lens. One cannot claim that the Russian state does not take any steps to liberalize “migratory regulation”; however, the effect of these measures is being vastly reduced by new restrictions. In addition, liberally designed laws do not bring the expected results due to the corrupt practices that pervade the whole of migration regulation.

The African society is one of the societies with rich culture and traditions. Apart from the indigenous religion of Africa, Christianity and Islam are worshiped as the major religions of the African society. Literature reflects a great amount of influence of religions on the existing societies, people and cultures. African literature often mirrors the clash of indigenous religion with Christianity. In the writings of African authors one can find the elements of Christian beliefs and practices. The present paper, however, is focused on the African woman novelist Buchi Emecheta’s selected four novels: Second-Class Citizen (1974), The Bride Price (1976), The Slave-Girl (1977) and The Joys of Motherhood (1979). The paper attempts to discuss the impact of Christianity on the social and cultural aspects of the African society with special focus on African women. The findings reveal the positive as well as negative impacts of the new religion on African people and on the position of African women through the characters present in the selected novels. With the medium of writing and through Christianity, Emecheta seek to educate her society and improve upon the position of the African women.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a frequent, debilitating and still enigmatic disease. There is a broad overlap in the symptomatology of ME/CFS and the Post-COVID Syndrome (PCS). A fraction of the PCS patients develops the full clinical picture of ME/CFS. New observations in microvessels and blood from patients suffering from PCS have appeared and include microclots and malformed pathological blood cells. Capillary blood flow is impaired not only by pathological blood components but also by prothrombotic changes in the vascular wall, endothelial dysfunction, and expression of adhesion molecules in the capillaries. These disturbances can finally cause a low capillary flow and even capillary stasis. A low cardiac stroke volume due to hypovolemia and the inability of the capacitance vessels to adequately constrict to deliver the necessary cardiac preload generate an unfavorable low precapillary perfusion pressure. Furthermore, a predominance of vasoconstrictor over vasodilator influences exists, in which sympathetic hyperactivity and endothelial dysfunction play a strong role, causing constriction of resistance vessels and of precapillary sphincters which leads to a fall in capillary pressure behind the sphincters. The interaction of these two precapillary cardiovascular mechanisms causing a low capillary perfusion pressure is hemodynamically highly unfavorable in the presence of a primary capillary stasis already caused by the pathological blood components and their interaction with the capillary wall, to severely impair organ perfusion. The detrimental coincidence of the microcirculatory with the precapillary cardiovascular disturbances may constitute the key disturbance of the Post-COVID-19 syndrome and finally lead to ME/CFS in pre-disposed patients because the interaction causes a particular kind of perfusion disturbance - capillary ischemia-reperfusion - which has a high potential of causing mitochondrial dysfunction by inducing sodium- and calcium-overload in skeletal muscles. The latter in turns worsens the vascular situation by the generation of reactive oxygen species to close a vicious cycle from which the patient can hardly escape.

Post-stroke depressive disorders (PSD) and post-stroke cognitive impairments (PCI) are frequent consequences of ischemic stroke (IS). The study was focused on exploring possible associations between relative volumes of cortical and limbic brain structures during the acute period of IS, and changes in biochemical indices of hypothalamic-pituitary-adrenal, sympathoadrenal medullary and inflammatory systems, with the development of PSD or PCI after mild or moderate IS. Patients developing PSD later on had significantly smaller relative volumes of the hippocampus, entorhinal cortex, and temporal pole versus patients without depressive symptoms. PCI development was associated with significantly smaller volumes of temporal pole and supramarginal gyrus versus patients without cognitive changes. Multiple logistic regression analysis showed higher likelihood of developing PSD in patients with smaller temporal pole volume (β0=10.9; β=-4.27; p=0.04) and increased salivary α-amylase activity (β0=-3.55; β=2.68e-05; p=0.02,). PCI likelihood was higher in patients with smaller supramarginal gyrus volume (β0=3.41; β=-0.99; p=0.047), smaller temporal pole volume (β0=3.41; β=-3.12; p=0. 06), and increased hair cortisol concentration at admission (index of accumulated stress load within a month before IS; β0=3.41; β=-0.05; p=0.08). The data support the hypothesis suggesting predisposition to PSD and PCI and multi hit scenarios of their pathogenesis with IS providing a final hit.

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. Prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart-sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart-sensing chairs, with a specific focus on the strategies used for posture detection and classification, as well as the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, and Google Scholar databases yielded 34 pertinent studies that utilize non-invasive methods for posture monitoring. The analysis reveals that Force Sensing Resistors (FSR) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy, due to constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.

Light detection and ranging (LIDAR) sensors using a polarization-diverse receiver are able to capture polarimetric information about the target under measurement. This information can be used in a classifier to make an instantaneous assessment of the type of material present, enabling a shot-by-shot prediction of the material type and distance to the target.

In 2022, the new WHO Classification of Endocrine and Neuroendocrine Tumors, Fifth Edition (beta version) (WHO 5th) was published. The importance of understanding the molecular genetics of thyroid tumors as revealed by large-scale genomic analyses such as The Cancer Genome Atlas (TCGA). Consequently, the WHO 5th was fundamentally revised, resulting in a systematic classification based on tumor cell-of-origin and clinical risk. This paper outlines following critical points of the WHO 5th. 1. Genetic mutations in follicular cell-derived neoplasms (FDN) highlight the role of mutations in the MAP kinase pathway, including RET, RAS, and BRAF, as drivers of carcinogenesis. Differentiated thyroid cancers such as follicular thyroid carcinoma (FTC) and papillary thyroid carcinoma (PTC) have specific genetic alterations that correlate with morphological classifications: RAS-like tumors (RLTs) and BRAF p.V600E-like tumors (BLTs), respectively. 2. The Framework for benign lesions have revised. The WHO 5th introduces new categories such as "developmental abnormalities" and "thyroid follicular nodular disease" in benign FDN. 3. Low-Risk Tumors include NIFTP, TT-UMP, and HTT. 4. PTC histological variants are reclassified as "subtypes" in the WHO 5th. 5. The concept of high-grade carcinomas is introduced, encompassing poorly differentiated thyroid carcinoma (PDTC), differentiated high-grade thyroid carcinoma (DHGTC), and high-grade medullary thyroid carcinoma (MTC). 6. Squamous cell carcinoma is included in anaplastic thyroid carcinoma (ATC) in the WHO 5th due to shared genetic and prognostic features. 7. Hürthle cell adenoma/carcinoma is renamed oncocytic thyroid adenoma/carcinoma. 8. Other miscellaneous tumors are classified into salivary gland-type carcinomas of the thyroid, thyroid tumours of uncertain histogenesis, thymic tumours within the thyroid, and embryonal thyroid neoplasms. The WHO 5th thus emphasizes the importance of classifying tumors based on genetic abnormalities together with histomorphology, aiding in accurate pathological diagnosis and treatment selection, including molecular-targeted therapies.

The rise in both the frequency of natural disasters and the ubiquity of artificial intelligence has led to novel applications of new technologies in improving disaster response processes, such as the labor-intensive assessment of disaster damages. Assessment of residential and commercial structure damages, a precursory step to government agencies being able to provide most of their financial assistance, has benefited from aerial and satellite imagery-based computer vision models; however, limitations of using such imagery include the ground structures being obscured by clouds or smoke, as well as the lack of resolution to distinguish individual structures from others. Using a different data source, we propose a damage severity classification model using ground-level imagery, focusing on residential structures damaged by wildfires. This classifier, a Vision Transformer (ViT) model trained on over 18,000 professionally labeled images of damaged homes from the 2020-2022 California wildfires, has achieved an accuracy score of over 95%. Further, we have open sourced the training dataset–the first of its kind and scale–as well as built a publicly available web application prototype, which we demoed to and received feedback from disaster response officials, both of which further contribute to the broader literature beyond the proposed model.

Vehicular traffic in urban areas continues to be a constant challenge today, due to the continuous population growth and the increase of vehicles in circulation. Vehicular congestion, as a recurrent problem, generates a negative impact on urban mobility and citizens’ quality of life. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns and an indicator was applied to identify and evaluate the congestion conditions of the areas. The results show a high precision and recall in congestion classification compared to a method based on static cells.

Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research is to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aims to meet three main objectives. These objectives are to identify the best regression model, the best classification model, and the best learning strategy that highly suits sleep disorder datasets. Considering two related datasets and several evaluation metrics that are related to the tasks of regression and classification, the results revealed the superiority of the MultilayerPerceptron, SMOreg, and KStar regression models compared with the other twenty-three regression models. Also, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belong to several learning strategies. Finally, the Function learning strategy showed the best predictive performance among the six considered strategies in both datasets and with respect to the most evaluation metrics.

In recent years, ozone (O3) pollution has been rapidly spreading, restricting further improvement of air quality in China. Investigating the interaction of O3 concentration and identifying their driven cities are important for the prevention and control of O3 pollution in China. However, the complex interaction between O3 pollution between the cities and their driven cities has not yet been revealed. In this study, we address this gap based on complex network methods. Specifically, an ozone relational network is constructed using an association calculation method. The driven nodes and spatial clusters were analyzed based on the maximum matching algorithm and the Louvain algorithm. The findings of the study reveal an aggregation phenomenon in the ozone network concerning distance. Furthermore, as the threshold Tc varies, the proportion of driven nodes exhibits a positive correlation. Moreover, a closer threshold value corresponds to a higher coincidence ratio of the driven nodes. The results provide scientific guidance for national O3 pollution prevention and regional synergy formatting. Furthermore, the introduced network-based approaches offer a mythological framework for the study of air pollution in key cities and clusters.

Software-Defined Networking (SDN) is a network implementation paradigm of great importance, as it has a profound impact on the pace of technological progress. While SDN doesn’t directly address the technical complexities of routing, congestion control, traffic engineering, security, mobility, reliability, or real-time communication, it paves the way for innovative solutions to emerge for these and similar challenges. Security is of utmost importance in SDN with Distributed Denial of Service (DDoS) being an attack which creates large scale problems. DDoS creates malicious traffic that resembles normal traffic in order to create service problems. As such, mechanisms that distinguish between benign and malicious traffic is essential, since this is the first step to mitigate the problem of DDoS. In this paper, we take a dataset onboard which exhibits benign and malicious traffic in SDN. There are 23 features that are used for classification purposes. Here we utilise classification procedure based on three methods based Grammatical Evolution applied on the aforementioned data. We provide results that show the efficiency of our approach and show that all three methods exhibits satisfactory results.

Numerous companies frequently engage in collaborative cooperation to exchange intelligence rather than resorting to competing strategies to achieve higher levels of intelligence. \textit{Intelligence} is essential in determining \textit{collaborative intelligence} . Intelligence can be discerned by a range of behaviors and actions that go beyond the mere individual observable activity. Collaborative intelligence is often defined as the combined actions of individuals (such as humans and machines) working towards a common goal. It encompasses more than just behaviour and includes other crucial elements such as the sharing of intelligence. The ontological view concerns the system's understanding and representation of information, data, and the fundamental reality it aims to capture and manage. This paper aims to expand upon our previous research on Intelligence in decentralized environments. The methodology utilized in this paper involves employing an ontological view to derive intelligence, encompassing its various forms, such as data, information, and knowledge. The paper explores the fundamental significance of intelligence and semantic integration in supporting the collaborative intelligence framework. Semantic integration is crucial to establishing a shared understanding of individuals' data, information, and concepts. It is fundamental in facilitating effective communication, intelligence sharing, and decision-making within a collaborative environment.

Chronic rhinosinusitis (CRS) can have a significant impact on quality of life. With persistent symptoms and the failure of initial medical treatments, surgical management is indicated. Despite the excellent results of endoscopic sinus surgery for persistent CRS, it is quite a challenging procedure for frontal sinusitis given the complex anatomy and location of the frontal sinus. Frontal sinus cells significantly contribute to the complexity of the frontal sinus, and numerous studies have sought to establish their association with sinusitis. This review offers a comprehensive understanding of the frontal sinus cells, their different classifications, their prevalence among different populations, and their relationship to sinusitis. After an extensive review of the current literature, the International Frontal Sinus Anatomy Classification (IFAC) is the most recent classification method and a preferred practical preoperative assessment tool. Although the Agger Nasi Cell is the most prevalent cell among all reported populations, ethnic variations are still influencing the other cells' distribution. Studies are inconsistent in reporting a relationship between frontal sinus cells and sinusitis, and that is mainly because of the difference in the classification method used. More research using a standardized classification method is needed to understand the association between frontal sinus cells and sinusitis.

Background: Pancreatic cancer (PC) is a disease with poor prognosis and survival rate. There is a pertinent need to identify the risk factors of this disease. The purpose of this study is to identify a subset of factors (a.k.a. features) as predictors of PC from the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer dataset consisting of responses to 65 questions about demographics, cancer and health history, medication usage, and smoking habits from 154,897 participants. Method: There are two challenges to selecting the subset of features that predict PC with highest probability: the problem is computationally intractable, and the PLCO dataset is highly imbalanced. We use an innovative method to use the dataset in a balanced way, without involving up- or down-sampling. We use nine feature selection methods to select the optimal subset of features from the preprocessed and balanced dataset. Results: Our preprocessed dataset consists of 32 risk factors (8 demographics, 5 cancer history, 13 health history, 2 medication usage, 4 smoking habits). Risk factors belonging to cancer and health history, followed by smoking habits, were consistently chosen by the feature selection methods. We also discuss findings in the medical sciences literature that corroborate our findings. Conclusions: The study found that risk factors belonging to cancer and health history are the most prominent ones for PC. In particular, previously diagnosed with PC is chosen as the most prominent risk factor by majority of methods. While most of our findings are consistent with the literature, some of our findings shed light on novel factors that may not have received their due attention by the research community.

It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.

Partial least square (PLS) regression is a well-known chemometric method used for predictive modelling, especially in the presence of many variables. Although PLS was not initially developed as a technique for classification tasks, scientists have reportedly used this approach successfully for discrimination purposes. Whereas some non-supervised learning approaches including but not limited to PCA, and k-means clustering do well in identifying/understanding grouping and clustering patterns in multidimensional data, they are limited when the end target is discrimination, making PLS a preferable alternative. A total of fertilized 672 chicken egg hyperspectral imaging data, consisting of 336 white eggs and 336 brown eggs were used in this study. Hyperspectral images in the NIR region of 900-1700 nm wavelength range were captured prior to incubation on day 0 and on days 1-4 after incubation. Eggs were candled on incubation day 5 and broken out on day 10 to confirm fertility. While a total number of 312 and 314 eggs were found to be fertile in the brown and white egg batches respectively, total numbers of non-fertile eggs in the same set of batches were 23 and 21 respectively. Spectral information was extracted from a segmented region of interest (ROI) of each hyperspectral image and spectral transmission characteristics were obtained by averaging the spectral information. A moving-thresholding technique was implemented for discrimination based on PLS regression results on the calibration set. With true positive rates (TPR) of up to 100% obtained at selected threshold values of between 0.50-0.85 and on different days of incubation, the results indicated that the proposed PLS technique can accurately discriminate between fertile and non-fertile eggs. The adaptive PLS approach was thereby presented as suitable for handling hyperspectral imaging-based chicken egg fertility data

In this paper, we propose a partial differential equation model based on phase-field for sentiment analysis in the field of hotel comment texts. The comment texts are converted into word vectors using the Word2Vec tool, and then we utilize the multifractal detrended fluctuation analysis (MF-DFA) model to extract the generalized Hurst exponent of the word vector time series to achieve dimensionality reduction of the word vector data. The dimensionality reduced data is represented in a two-dimensional computational domain, and the modified Allen-Cahn (AC) function is used to evolve the phase values of the data to obtain a stable nonlinear boundary, thereby achieving automatic classification of hotel comment texts. The experimental results show that the proposed method can effectively classify positive and negative samples and achieve excellent results in classification indicators. We compared our proposed classifier with traditional machine learning models and the results indicate that our method possesses a better performance.

It is difficult to obtain necessary information accurately from Social Networking Service (SNS) while raising children, and it is thought that there is a certain demand for the development of a system that presents appropriate information to users according to the child's developmental stage. There are still few examples of research on knowledge extraction that focuses on childcare. This research aims to develop a system that extracts and presents useful knowledge for people who are actually raising children, using texts about childcare posted on Twitter. In many systems numbers in text data are just strings like words and are normalized to zero or simply ignored. In this paper, we created a set of tweet texts and a set of profiles created according to the developmental stages of infants from "0-year-old child" to "6-year-old child". For each set, we used ML algorithms such as NB (Naive Bayes), LR (Logistic Regression), ANN (Approximate Nearest Neighbor algorithms search), XGboost, RF (random forest), decision trees, and SVM (Support Vector Machine) to compare with BERT, a neural language model, to construct a classification model that predicts numbers from "0" to "6" from sentences. The accuracy rate predicted by the BERT (Bidirectional Encoder Representations from Transformers) classifier was slightly higher than that of the NB, LR, ANN, XGboost, RF, decision trees, and SVM classifiers, indicating that the BERT classification method was better.

To solve the problem that the common long-tailed classification method does not use the semantic features of the original label text of the image, and the difference between the classification accuracy of most classes and minority classes is large, the long-tailed image classification method based on enhanced contrast visual language trains the head class and tail class samples separately, uses text image to pre-train the information, and uses enhanced momentum contrast loss function and RandAugment enhancement to improve the learning of tail class samples. On the ImageNet-LT long-tailed dataset, the enhanced contrastive visual-language based long-tailed image classification method has improved all class accuracy, tail class accuracy, middle class accuracy, and F1 values by 3.4%, 7.6%, 3.5%, and 11.2%, respectively, compared to the BALLAD method. The difference in accuracy between the head class and tail class is reduced by 1.6% compared to the BALLAD method. The results of three comparative experiments indicate that the long-tailed image classification method based on enhanced contrastive visual-language has improved the performance of tail classes and reduced the accuracy difference between majority and minority classes.

Effective healthcare relies on accurate and timely diagnosis; however, obtaining large amounts of training data while maintaining patient privacy remains challenging. This study introduces a novel approach utilizing federated learning (FL) and a cross-device multi-modal model for clin-ical event classification using vital signs data. Our architecture leverages FL to train machine learning models, including Random Forest, AdaBoost, and SGD ensemble model, on vital signs data from a diverse clientele at a Boston hospital (MIMIC-IV dataset). The FL structure preserves patient privacy by training directly on each client's device without transferring sensitive data. The study demonstrates the potential of FL in privacy-preserving clinical event classification, achieving an impressive accuracy of 98.9%. These findings underscore the significance of FL and cross-device ensemble technology in healthcare applications, enabling the analysis of large amounts of sensitive patient data while safeguarding privacy.

Crop disease classification has always been a critical and persistent problem in the field of agricultural and forestry sciences, where often we do not have access to a sufficient number of samples to know the distribution of real-world samples. How to make full use of the existing data is the starting point of our thinking. To address this problem, this paper proposes a supervised image augmentation method Negative Contrast, which uses the contrast images of existing disease samples after removing disease areas as negative samples for image augmentation when samples are relatively scarce. Numerous experiments have shown that several classical models using this augmentation method have improved in disease classification of four crops, rice, wheat, corn, and soybean, with a maximum accuracy improvement of 30.8%. In addition, the comparative analysis of attentional heat map shows that the model using Negative Contrast is more accurate and intense on the area of interest of diseases, and thus reflects better generalization ability in real-world disease classification. Our dataset and codes can be found in https://www.kaggle.com/datasets/w970704112/corn-wheat-rice-soybean and https://github.com/hiter0/contrastaug .

To effectively preserve marine environments and manage endangered species, it is necessary to employ efficient, precise, and scalable solutions for environmental monitoring. Ecoacoustics provides several benefits as it enables non-intrusive, prolonged sampling of environmental sounds, making it a promising tool for conducting biodiversity surveys. However, analyzing and interpreting acoustic data can be time-consuming and often demands substantial human supervision. This challenge can be addressed by harnessing contemporary methods for automated audio signal analysis, which have exhibited remarkable performance due to advancements in deep learning research. This paper introduces a research investigation into developing an automatic computerized system to detect dolphin whistles. The proposed method utilizes a fusion of various resnet50 networks integrated with data augmentation techniques. Through extensive experiments conducted on a publically available benchmark, our findings demonstrate that our ensemble yields significant performance enhancements across all evaluated metrics. The MATLAB/PyTorch source code is freely available at: https://github.com/LorisNanni/

Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performing this task on a large scale can be time-consuming and error-prone. Therefore, automating this process using neural networks can save time and effort while achieving higher accuracy. Our study focuses on the classification step of human chromosome karyotyping, an important medical procedure that helps diagnose genetic disorders. Traditionally, this task is performed manually by expert cytologists, which is a time-consuming process that requires specialized medical skills. Therefore, automating it through deep learning can be immensely useful. To accomplish this, we implemented and adapted existing preprocessing and data augmentation techniques to prepare the chromosome images for classification. We used ResNet-50 convolutional neural networks and an ensemble approach to classify the chromosomes, obtaining state-of-the-art performance in the tested dataset.

With the increasing utilization of intelligent mobile devices for online inspection of electrical equipment in smart grids, the limited computing power and storage capacity of these devices pose challenges for deploying large algorithm models and it’s hard to obtain a substantial number of images of electrical equipment in public. In this paper, we propose a novel distillation method that compresses the knowledge of teacher networks into a compact few-shot classification network, employing a global and local knowledge distillation strategy. Central to our method is exploiting the global and local relationship between the features exacted by the backbone of the teacher network and student network. We compare our method with recent state-of-the-art (SOTA) methods on three public datasets and achieve superior performance. Additionally, we contribute a new dataset, namely EEI-100, which is specifically designed for classification of electrical equipment. We validate our method on this dataset and demonstrate its exceptional prediction accuracy of 94.12% when utilizing only 5-shot images.

It is difficult to obtain necessary information accurately from Social Networking Service (SNS) while raising children, and it is thought that there is a certain demand for the development of a system that presents appropriate information to users according to the child's developmental stage. There are still few examples of research on knowledge extraction that focuses on childcare. This research aims to develop a system that extracts and presents useful knowledge for people who are actually raising children, using texts about childcare posted on Twitter. In many systems numbers in text data are just strings like words and are normalized to zero or simply ignored. In this paper, we created a set of tweet texts and a set of profiles created according to the developmental stages of infants from "0-year-old child" to "6-year-old child". For each set, we used Support Vector Machine (SVM), and Bidirectional Encoder Representations from Transformers (BERT), a neural language model, to construct a classification model that predicts numbers from "0" to "6" from sentences. The accuracy rate predicted by the BERT classifier was slightly higher than that of the SVM classifier, indicating that the BERT classification method was better.

Computer vision methods have shown to be effective in classifying garbage into recycling categories for waste processing, existing methods are costly, imprecise, and unclear. To tackle this issue, we introduce MWaste, a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard. Its effectiveness was tested on various neural network architectures and real world images, achieving an average precision of 92% on the test set. This app can help combat climate change by enabling efficient waste processing and reducing the generation of greenhouse gases caused by incorrect waste disposal.

Psychomotor developmental delay in infants includes failure to acquire abilities such as sitting, walking, grasping objects and communication at the ages when most infants have acquired these abilities. Known risk factors include a large number of aspects of family environment, socioeconomic position, problems in pregnancy and birth, and maternal health. It is clinically useful to be able to screen for developmental delay so that healthcare interventions can be considered. The present research used machine learning (random forest) to create an algorithm predicting psychomotor delay in 9-month-old infants using information ascertainable at birth and in early infancy. The dataset was the UK longitudinal Millennium Cohort study. Fifty-two predictors measuring socioeconomic indicators, paternal, family and social support for the mother, beliefs about good parenting, maternal health, pregnancy and birth were included in the initial algorithm. Feature reduction showed that of the 52 variables, birthweight, family income and parents’ ages had the highest feature importance scores and could alone correctly predict developmental delay with over 99% sensitivity and 100% specificity. The relationships between delay and some of the predictors, particularly income, were nonlinear and complex. The results suggest that the risk of psychomotor developmental delay can be identified in early infancy using machine learning, and that the best predictors are factors present prior to birth. Surprisingly, the most important factors included in the present study did not include illnesses during pregnancy such as eclampsia and infections.

Salt marshes are globally important ecosystems, but many have been lost or transformed due to the impacts of global change. There have been attempts to broadly quantify salt marsh communities, especially the ubiquitous grasses which serve as foundation species such as Spartina alterniflora and Spartina patens, the latter of which is being lost due to sea level rise. However, few researchers have used high-resolution geospatial imagery to quantify fine-scale changes in the distribution of grasses or to track losses of S. patens. To address this issue, we utilized a simple and rapid method of classifying geospatial marsh imagery with cloud-based machine learning in Google Earth Engine (>92% accuracy for S. patens regardless of imagery age). Our methods allowed us to characterize full landscapes (two geospatially proximal areas, >7,000 ha each) of critical salt marshes on the New Jersey coast and to evaluate fine-scale (1-m) community transformations in response to global change with imagery from 2006 to 2019. Notably, one marsh experienced very little change while the other experienced an 81.17% (1,087 ha) loss of S. patens, illuminating disparate patterns of change for two geographically proximal ecosystems. Further exploration revealed an association in the loss of S. patens with increases in streamflow and total nitrogen content in the rivers that run through each marsh. These results signify the importance of broad-scale ecological studies that evaluate fine-scale community transformations and for management strategies that do not generalize across landscapes of an ecosystem-type.

One of the most common forms of dementia is Alzheimer’s disease (AD), which leads to progressive mental deterioration. Unfortunately, there is no definitive diagnosis and cure that can stop the condition progressing. The diagnosis is often performed based on the clinical history and neuropsychological data, including magnetic resonance imaging (MRI). Deep neural networks (DNN) algorithms are gaining popularity for medical diagnosis, and have been used widely for the analysis of MRI data. DNNs can extract hidden features from thousands of training images automatically. However, they cannot judge how confident they are about their predictions. To use DNNs in safety-critical applications such as medical diagnosis, uncertainty quantification of DNNs predictions is crucial. For this purpose, Monte Carlo dropout (MCD) has been widely used, however, it may lead to overconfident and miss calibrated results. This paper proposes a framework in which the MCD algorithm’s hyper-parameters are optimized during training using Bayesian optimization for the first time. The conducted optimization leads to assigning high predictive entropy to erroneous predictions and making it possible to recognize risky predictions. The proposed framework is used for AD diagnosis, which has not been done before. We compare our method with some existing methods in the literature based on different uncertainty quantification criteria. The results of comprehensive experiments on the Kaggle dataset using a deep model pre-trained on the ImageNet dataset show that the proposed algorithm can quantify uncertainty much better than the existing methods.

The electroretinography (ERG) is a diagnostic test that measures the electrical activity of the retina in response to a light stimulus. This test has a long history and has been extensively studied among years. Dr. James Dewar recorded the first electroretinogram signal in 1873. Later he described signal analysis using 4 components, namely amplitude, and latency of a-wave and b-wave. Nowadays, the international electrophysiology community has established the standard for electroretinography in 2008. However, from the point of view of signal analysis, the major change did not happen. ERG analysis is still based on the 4 components evaluation. The article describes ERG database including the classification of signals by using advanced analysis of electroretinograms based on wavelet scalogram processing. To implement an extended analysis of the ERG, the parameters extracted from the wavelet scalogram of the signal were obtained using digital image processing and machine learning methods. The results of the study show that the proposed algorithm implements the classification of adult electroretinogram signals by 19% more accurately and pediatric signals by 20% more accurately than the classical algorithm. The promising use of ERG is differential diagnostics, which may also be used in preclinical toxicology and experimental modeling. The problem of developing methods for electrophysiological signals analysis in ophthalmology is associated with the complex morphological structure of electrophysiological signals components, due to the generation of retina cell electrical responses to light stimulus.

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision (YOLO) and Term Frequency-Inverse Document Frequency (TF-IDF). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.

We describe a dataset that contains job description published on a popular online website in the information and technology sector. As the website focus mainly on United Kingdom based jobs, the data have a specific focus on this country. It contains 11.501 job vacancies and 13 related meta data information. The dataset is suitable for HR analysis using machine learning techniques such as natural language processing and neural networks.

We are of the opinion that during the design of a binary classifier one ought to consider adding an “I don’t know” answer. We provide the case for the introduction of this third category when a human needs to make a decision based on the answer from a binary classifier. We discuss the costs and potential benefits of its introduction. Colloquially, we have used the term “I don’t know”, but formally we refer to it as NotAvailable. A procedure to define NotAvailable predictions in binary classifiers, called all leave-one-out models (ALOOM), is presented as proof of the concept. Furthermore, we discuss the potential benefits of applying ALOOM in real life applications.

Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.

The widespread deployment of social media has helped researchers access an enormous amount of data in various domains, including the pandemic caused by the COVID-19 spread. This study presents a heuristic approach to classify Commercial Instagram Posts (CIPs) and explores how the businesses around the Holy Shrine – a sacred complex in Mashhad, Iran, surrounded by numerous shopping centers – were impacted by the pandemic. Two datasets of Instagram posts (one gathered data from March 14th to April 10th, 2020, when Holy Shrine and nearby shops were closed, and one extracted data from the same period in 2019), two word embedding models – aimed at vectorizing associated caption of each post, and two neural networks – multi-layer perceptron and convolutional neural network – were employed to classify CIPs in 2019. Among the scenarios defined for the 2019 CIPs classification, the results revealed that the combination of MLP and CBoW achieved the best performance, which was then used for the 2020 CIPs classification. It is found out that the fraction of CIPs to total Instagram posts has increased from 5.58% in 2019 to 8.08% in 2020, meaning that business owners were using Instagram to increase their sales and continue their commercial activities to compensate for the closure of their stores during the pandemic. Moreover, the portion of non-commercial Instagram posts (NCIPs) in total posts has decreased from 94.42% in 2019 to 91.92% in 2020, implying the fact that since the Holy Shrine was closed, Mashhad citizens and tourists could not visit it and take photos to post on their Instagram accounts.

Artificial Intelligence (AI) has seen increased application and widespread adoption over the past decade despite, at times, offering a limited understanding of its inner working. AI algorithms are, in large part, built on weights, and these weights are calculated as a result of large matrix multiplications. Computationally intensive processes are typically harder to interpret. Explainable Artificial Intelligence (XAI) aims to solve this black box approach through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies chronic wounds through the use of transfer learning and fully connected layers. Classified chronic wound images serve as input to the XAI model for an explanation. Interpretable results can help shed new perspectives to clinicians during the diagnostic phase. The proposed method successfully provides chronic wound classification and its associated explanation. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes.

Seismic imaging is the main technology used for subsurface hydrocarbon prospection. It~provides an image of the subsurface using the same principles as ultrasound medical imaging. As for any data acquired through hydrophones (pressure sensors) and/or geophones (velocity/acceleration sensors), the raw seismic data are heavily contaminated with noise and unwanted reflections that need to be removed before further processing. Therefore, the noise attenuation is done at an early stage and often while acquiring the data. Quality control (QC) is mandatory to give confidence in the denoising process and to ensure that a costly data re-acquisition is not needed. QC is done manually by humans and comprises a major portion of the cost of a typical seismic processing project. It is therefore advantageous to automate this process to improve cost and efficiency. Here, we propose a supervised learning approach to build an automatic QC system. The~QC system is an attribute-based classifier that is trained to classify three types of filtering (mild = under filtering, noise remaining in the data; optimal = good filtering; harsh = over filtering, the signal is distorted). The attributes are computed from the data and represent geophysical and statistical measures of the quality of the filtering. The system is tested on a full-scale survey (9000 km²) to QC the results of the swell noise attenuation process in marine seismic data.

In this paper, we have analysed the level of advancement in circular economy (CE) in the EU-28 countries. Firstly, we used a synthetic measure to examine CE advancement in EU countries in each of the Eurostat CE distinguished areas, i.e. production and consumption, waste management, secondary raw materials, and competitiveness and innovation. For the empirical analysis, we applied 17 Eurostat indicators to the CE areas. To find the synthetic measure in 2010, 2012, 2014, and 2016, we used multidimensional comparative analysis, i.e. a zero unitarisation method. Secondly, based on the synthetic measures of the CE areas, we created a general synthetic measure of the CE advancement of the EU-28 countries as well as the countries’ rankings. Thirdly, we classified the countries into groups according to their level of advancement in CE, i.e. high level, medium-high level, medium-low level, and low level groups. Finally, we applied a similarity measure to evaluate the correlation between obtained rankings in two most extreme moments in the period of analysis (2010, 2016). Our analysis covers all EU member states, as well as "old" and "new" EU countries separately. Our results confirm that highly developed Benelux countries, i.e. Luxembourg, the Netherlands, and Belgium, have the highest CE advancement level. Malta, Cyprus, Estonia, and Greece are the least advanced in CE practice. Apart from that, on average, there is some progress in CE implementation, significant disproportions between the EU countries were observed, especially among the "new" member states.

Seismic imaging is the main technology used for subsurface hydrocarbon prospection. It provides an image of the subsurface using the same principles as ultrasound medical imaging. It is based on emitting a sound (pressure) wave through the subsurface and recording the reflected echoes using hydrophones (pressure sensors) and/or geophones (velocity/acceleration sensors). Contrary to medical imaging, which is done in real time, subsurface seismic imaging is an offline process that involves a huge volume of data and needs considerable computing power. The raw seismic data are heavily contaminated with noise and unwanted reflections that need to be removed before further processing. Therefore, the noise attenuation is done at an early stage and often while acquiring the data. Quality control (QC) is mandatory to give confidence in the denoising process and to ensure that a costly data re-acquisition is not needed. QC is done manually by humans and comprises a major portion of the cost of a typical seismic processing project. It is therefore advantageous to automate this process to improve cost and efficiency. Here, we propose a supervised learning approach to build an automatic QC system. The QC system is an attribute-based classifier that is trained to classify three types of filtering (mild = underfiltering, noise remaining in the data; optimal = good filtering; harsh = overfiltering, the signal is distorted). The attributes are computed from the data and represent geophysical and statistical measures of the quality of the filtering. The system is tested on a full-scale survey (9000 km2) to QC the results of the swell noise attenuation process in marine seismic data. The results are encouraging and helped identify localized issues that were difficult for a human to spot.

Bulgaria and North Macedonia have a long history of production and use of honey, however, there is an obvious lack of systematic and in-depth research on honey from both countries. Of particular interest is the oak honeydew honey, highly valued by consumers because of its health benefits. Aim of this study was to characterize honeydew and floral honeys from Bulgaria and North Macedonia based on their NMR profiles. 1D and 2D 1H and 13C NMR spectra were measured of 16 North Macedonian and 22 Bulgarian honey samples. 25 individual substances were identified, including quinovose, which was found for the first time in honey. Chemometric methods (PCA - principal component analysis, PLS-DA - partial least squares discriminant analysis, ANOVA) were used to detect similarities and differences between samples, as well as to determine their botanical and geographical origin. Semiquantitative data on individual sugars and some other constituents were obtained; which allowed reliable classification of honey samples by botanical and geographical origin, based on chemometric approaches. The results enabled to distinguish oak honeydew honey from other honey types, and to determine the country of origin. NMR was a rapid and convenient method, avoiding the need for other more time-consuming analytical techniques.

Psoriasis is a chronic, multisystem, inflammatory disease. The typical clinical cutaneous manifestation of psoriasis is scaly erythema or plaque, limited or widely distributed. However, psoriasis is far beyond the skin involvement and provides many challenges, including associated comorbidities. In this review, we classify psoriasis as cutaneous and systemic psoriasis, according to the clinical diversity and associated comorbid diseases, and recommend classification criteria for psoriasis. The key objective of this novel classification is to raise awareness of the complexity of this multifaceted disease and help to better understand and manage this complex disease comprehensively.

Predicting the popularity of posts on social networks has taken on significant importance in recent years, and several social media management tools now offer solutions to improve and optimize the quality of published content and to enhance the attractiveness of companies and organizations. Scientific research has recently moved in this direction, with the aim of exploiting advanced techniques such as machine learning, deep learning, natural language processing, etc., to support such tools. In light of the above, in this work we aim to address the challenge of predicting the popularity of a future post on Instagram, by defining the problem as a classification task and by proposing an original approach based on Gradient Boosting and feature engineering, which led us to promising experimental results. The proposed approach exploits big data technologies for scalability and efficiency and is general enough to be applied to other social media as well.

The article’s goal is to overview challenges and problems on the way from the state of the art CUDA accelerated neural networks code to multi-GPU code. For this purpose, the authors describe the journey of porting the existing in the GitHub, fully-featured CUDA accelerated Darknet engine to OpenCL. The article presents lessons learned and the techniques that were put in place to make this port happen. There are few other implementations on the GitHub that leverage the OpenCL standard, and a few have tried to port Darknet as well. Darknet is a well known convolutional neural network (CNN) framework. The authors of this article investigated all aspects of the porting and achieved the fully-featured Darknet engine on OpenCL. The effort was focused not only on the classification with the use of YOLO1, YOLO2, and YOLO3 CNN models. They also covered other aspects, such as training neural networks, and benchmarks to look for the weak points in the implementation. The GPU computing code substantially improves Darknet computing time compared to the standard CPU version by using underused hardware in existing systems. If the system is OpenCL-based, then it is practically hardware independent. In this article, the authors report comparisons of the computation and training performance compared to the existing CUDA-based Darknet engine in the various computers, including single board computers, and, different CNN use-cases. The authors found that the OpenCL version could perform as fast as the CUDA version in the compute aspect, but it is slower in memory transfer between RAM (CPU memory) and VRAM (GPU memory). It depends on the quality of OpenCL implementation only. Moreover, loosening hardware requirements by the OpenCL Darknet can boost applications of DNN, especially in the energy-sensitive applications of Artificial Intelligence (AI) and Machine Learning (ML).

The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within Polarimetric Synthetic Aperture Radar (PolSAR) images. This manuscript addresses two important aspects when applying such methods on real data, namely speckle filtering and statistical classification with ICA. A novel PolSAR data processing framework is introduced by adjusting the Lee's sigma filter to the particular nature of the Touzi's polarimetric decomposition. In its current form, it allows the use of the ICA mixing matrix in the derived speckle filter. An extension of the Fromont at al. iterative segmentation is introduced, equally. This proposed framework is tested using P band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.

Background: the primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: the study was intended to examine the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: 20 machine learning algorithms (based on 5 types of algorithms and 4 text representation techniques)were trained using a sample of 3,559 messages (169,102 words) corresponding to 2,268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: the best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables "avoiding the need of a face-to-face visit" and "increased demand" (precision = 0.98 and 0.97, respectively) rather than the variable "type of query"(precision = 0.48). Conclusion: to the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.

Road surface monitoring more specifically crack detection on the surface of the road pavement is a complicated task which is found vital due to critical nature of roads as elements of transportation infrastructure. Cracks on the road pavement is detectable using remotely sensed imagery or car mounted platforms. UAV’s are also considered as useful tools for acquiring reliable information about the pavement of the road. In This paper, an automatic method for crack detection on the road pavement is proposed using acquired videos from UAV platform. Selecting key frames and generating Ortho-image, violating non road regions in the scene are removed. Then through an edge based approach hypothesis crack elements are extracted. Afterwards, through SVM based classification true cracks are detected. Developing the proposed method, the generated results show 75% accuracy in crack detection while less than 10% of cracks are omitted.

Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple parameters. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate auto-prediction of eye diseases. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, different machine learning algorithms were used to analyze patient data based on multiple parameters, including age, illness history, and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily given a sufficient amount of data. The random forest and decision tree algorithms predicted more accurately as compared to neural networks and the na{\"i}ve Bayes algorithm owing to structured data arrangement.

Folk nomenclature is habitually established for species that have attained high utilitarian and cultural significance by custodians of such plants worldwide. Such folk names assigned to species often carry etymological values such as therapeutic effects, morphological features, mythical connotations, and their allegorical values. This research sought to unveil the etymology in folk nomenclatures of Sphenostysis stenocarpa (Hosch ex A. Rich) Harms (African Yam Bean). Three hundred and fifty respondents were randomly selected from 13 local communities in Ebonyi State in South-eastern, Nigeria. Data were collected through oral interviews with semi-structural questionnaires, along with focused group discussions. Analysis of data was carried out using simple statistical methods involving frequencies and percentages. The results recorded ten folk nomenclatures assigned to this species in seven dialects affiliated to cultural values within these communities. Etymologically, the results also revealed that out of the ten folk names of AYB cryptic connotations, five reflected their trust in the gods that answered their prayers, two were attributed to the healing potentials inherent in this crop for medicine, three names were associated with the seeds, while one referred to feminist attachment to the crop, another to its resilience/ adaptability to climatic stress and one as a sustainer of farmers. Considering that folk nomenclature is based mainly on qualitative data and the information outside the scientific domain, they are nonetheless highly valued because they are based on long-term interactions, utilization and observations of the custodians of these natural resources. However, these data are equally vulnerable to erosion if not properly documented and conserved for posterity.

In recent years, using multispectral cameras on UAVs has provided an opportunity to capture separate bands that offer the extraction of spectral features used for early detection of diseased plants. One of the main steps in disease detection is radiometric calibration that converts digital numbers to reflectance values commonly using white reference panels. This paper focused on the necessity of radiometric calibration to distinguish disease trees in orchards based on aerial multi-spectral images. For this purpose, two study sites with various climate conditions and tree species as well as different disease types were selected where multispectral images were taken using a multirotor UAV. The impact of radiometric correction on plant disease detection was assessed in two ways: 1) comparison of separability between the healthy and diseased classes using T-test and entropy distances; 2) radiometric calibration effect on the accuracy of classification. The experimental result showed the insignificant effect of radiometric calibration on separability criteria. Furthermore, based on T-test and entropy distances criteria, NIR and R spectral features made highest distances between healthy and Greening infected citrus trees, respectively, at the first study site while NDRE and BNDVI spectral features made highest distances between healthy and peach leaf curl infected trees, respectively, at the other study site. In the second strategy, the experimental result showed that radiometric calibration had no effect on the accuracy of classification. As a result, the overall accuracy and kappa values for both un-calibrated and calibrated orthomosaic classifications of the citrus orchard were 96.6% and 0.94%, respectively, using five spectral bands as well as DVI, NDRE, NDVI and GNDVI vegetation indices using a random forest classifier. The experimental results were also similar at the other study site. Therefore, the overall accuracy and kappa values for both the un-calibrated and calibrated orthomosaic classifications were 96.1%, 0.92, respectively, using five spectral bands as well as NDRE, BNDVI, GNDVI, DVI, and NDVI vegetation indices.

Disease classification based on biological data is an important area in bioinformatics and biomedical research. It helps the doctors and medical practitioners for the early detection of disease and support them as a computer-aided diagnostic tool for accurate diagnosis, prognosis, and treatment of disease. Earlier Microarray gene expression data have wide application for the classification of disease, but now Next-generation sequencing (NGS) has replaced the Microarray technology. From the last few years, RNA sequence (RNA-Seq) data are widely used for the transcriptomic analysis. Hence, RNA-Seq based classification of disease is in its infancy. In this article, we present a general framework for the classification of disease constructed on RNA-Seq data. This framework will guide the researchers to process RNA-Seq, extract relevant features and apply the appropriate classifier to classify any kind of disease.

Automated machine classifications of galaxies are necessary because the size of upcoming surveys will overwhelm human volunteers. We improve upon existing machine classification methods by adding the output of SpArcFiRe to the inputs of a machine learning model. We use the human classifications from Galaxy Zoo 1 (GZ1) to train a random forest of decision trees to reproduce the human vote distributions of the Spiral class. We prefer the random forest model over other black box models like neural networks because it allows us to trace post hoc the precise reasoning behind the classification of each galaxy. We find that, across a sample of 470,000 Sloan galaxies that are large enough that details could be seen if they were there, the combination of SpArcFiRe outputs with existing SDSS features provides a better machine classification than either one alone on comparison to Galaxy Zoo 1. We suggest that adding SpArcFiRe outputs as features to any machine learning algorithm will likely improve its performance.

We describe a multi-resolution approach for audio classification and illustrate its application to the open data set for environmental sound classification. The proposed approach utilizes a multi-resolution based ensemble consisting of targeted feature extraction of approximation (coarse scale) and detail (fine scale) portions of the signal under the action of multiple transforms. This is paired with an automatic machine learning engine for algorithm and parameter selection and the LSTM algorithm, capable of mapping several sequences of features to a predicted class membership probability distribution. Initial results show an improvement in multi-class classification accuracy.

Molecular classifications of colorectal cancer (CRC) are benefitting cancer research by providing insights into subtype-specific disease prognosis and better therapeutic intervention. So far different conventional DNA markers such as microsatellite instability (MSI), CpG island methylator phenotype (CIMP), chromosomal instability (CIN), and BRAF and KRAS mutations have been used to classify CRC patients but have not shown promising prognostic values. Here, for the first time, we show classification of CRC tumors from Saudi Arabian patients based on gene expression profile (GEP). An existing method of CRC subtyping has been applied to the GEP of tumors from Saudi CRC patients. Survival analysis was carried out on predicted CRC subtypes. In-silico functional analyses were conducted on the gene signature used for subtype prediction. The predicted subtypes showed distinct but statistically insignificant overall survival distribution (log-rank test, p = 0.069). Comparison of predicted subtypes in Saudi CRC patients with that of the French one showed significant dissimilarity in the two populations (Chi-square test, p = 0.0091). Functional analyses of the gene signature used for subtyping suggest their association with “cancer” and “gastrointestinal diseases”. Most of the signature genes were found differentially expressed in CRC tumors compared to adjacent normal tissues. Such a classification framework might help improve the treatment of colorectal cancer patients.

We propose a quantum version of the well known minimum distance classification model called "Nearest Mean Classifier" (NMC). In this regard, we presented our first results in two previous works. In [34] a quantum counterpart of the NMC for two-dimensional problems was introduced, named "Quantum Nearest Mean Classifier" (QNMC), together with a possible generalization to arbitrary dimensions. In [33] we studied the n-dimensional problem into detail and we showed a new encoding for arbitrary n-feature vectors into density operators. In the present paper, another promising encoding of n-dimensional patterns into density operators is considered, suggested by recent debates on quantum machine learning. Further, we observe a significant property concerning the non-invariance by feature rescaling of our quantum classifier. This fact, which represents a meaningful difference between the NMC and the respective quantum version, allows to introduce a free parameter whose variation provides, in some cases, better classification results for the QNMC. The experimental section is devoted to: i) compare the NMC and QNMC performance on different datasets; ii) study the effects of the non-invariance under uniform rescaling for the QNMC.

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Sixteen features are extracted and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and that collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values being greater than 95%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

Categorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. This study introduces the theoretical backgrounds of linear Bayesian updating (LBU) approach for spatial classification through expert system. Transition probabilities are interpreted as expert opinions for updating the prior marginal probabilities of categorical response variables. The main objective of this paper is to present the solid theoretical foundations of LBU and provide a categorical random field prediction method which yields relatively higher classification accuracy compared with conventional Markov chain random field (MCRF) approach. A real-world case study has also been carried out to demonstrate the superiority of our method. Since the LBU idea is originated from aggregating expert opinions and not restricted to conditional independent assumption (CIA), it may prove to be reasonably adequate for analyzing complex geospatial data sets, like remote sensing images or area-class maps.

The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning text mining algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm is trained by TFIDF bigram features which contribute a network training model. The algorithm is tested on two different real-world datasets from the CBC news network and Covid-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97-99 %. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents which may contribute negatively to the COVID-19 pandemic.

Frass generated during the production of black soldier fly larvae is taking the interest of scientists and horticultural producers because it is a material from the biotransformation of organic waste, it has several nutrients that can be used by plants, and its recent focus on its biostimulant capacity. The thermal-composting process is a stabilization that improves physical and chemical properties of treated wastes, allowing better performance in plants compared to its fresh state. In this research, thermocomposted frass was evaluated as a germination substrate for kale seeds (Brassica olerasea). To achieve this, it was evaluated the phytotoxicity in different concentrations on the seeds, and seedlings were grown in a germination substrates mixed with 20, 40, 60, 80 and 100% of frass. The treatment with 20% frass showed the highest values of seedling height, stem diameter, number of leaves, length, and width of first true leaf and length and width of cotyledons, reduced the content of phenols and tannins antioxidants but the content of flavonoids increased compared to the control and the rest of mixtures.

After the end of colonial rule in 1997, the Hong Kong education system has been experiencing a policy reform on Senior Secondary Education (NSS) Curriculum commenced in September 2009 aiming to educate students as globalized Hong Kong citizens for the Western. Although previous scholars argued that it is hard to differentiate between domestic values and the Western values in account of curriculum development (Sewpaul, 2007), this study demonstrates an analysis using the dichotomy to investigate how local curriculum, especially in the post-colonial areas, embrace both the Western values and domestic values. Using content analysis on the NSS curriculum guides, this article evaluates the influence of the Western values and Chinese values in the NSS curriculum through investigating: (1) how the Western values and Chinese values was incorporated into the curriculum throughout its development (2) why some values are more significantly highlighted in the curriculum guide. Findings include that more Chinese values are involved in the curriculum documents. This may be because of the upholding of domestic values as a post-colonial city. The Western values which are academically related are greatly incorporated into the local curriculum.

Research indicates that painful experiences can significantly affect the fear of cancer recurrence among cancer survivors, which is a distressing concern that influences both physiological and psychological recovery. Yet, the role of potential factors and mechanisms in these relationships is not fully known. This cross-sectional study aimed to advance our comprehension of the associa-tions between total pain and the fear of recurrence in post-treatment cancer patients by examining two potential mediators: psychological flexibility and mentalization. Three hundred and thirty-five participants (aged 22 to 88, 49.1% female) who had finished their cancer treatment completed self-report assessments of total pain, fear of recurrence, psychological flexibility, and mentalization. The serial mediation analysis showed that all dimensions of total pain were positively related to the fear of recurrence indirectly through psychological flexibility and mentalization, in serial. Additionally, gender moderated these serial mediational effects. In line with the psychological flexibility model, personal capacities to face difficult internal/external problems and interpret one’s behaviour in motivational terms can counterbalance patients’ negative emotions and feelings related to the illness.

Mitochondria are semiautonomous organelles, which are pivotal for the energy metabolism of eukaryotic cells. These organelles contain their own genetic system (mtDNA, mitogenome) that encodes to some structural RNAs and organellar proteins. The biogenesis and function of mitochondria is tightly regulated by nuclear-encoded protein cofactors. The mtDNAs of land plants are characteristically large, with a complex mode of gene organization and expression, in particularly at the post-transcriptional level. To become functional, the primary organellar transcripts undergo extensive maturation steps, which include endo- and exo-nucleolytic cleavage, RNA-base editing, and both ‘cis’- and ‘trans’-splicing events. These essential processing steps rely on the activities of a large set of nuclear-encoded factors. Among these, the RNA helicases serve as key players in organellar (mt-)RNA metabolism. This review summarizes the important roles played by these factors in regulating the highly dynamic processes of transcription, RNA processing, and translation within the mitochondria of land plants. We further discuss recent advancements in understanding how dysregulation of mitochondrial RNA helicases affects the organellar gene expression, respiratory functions, and hence the growth, development and physiology of land plants.

P. palmata is a viable source of nutrients with bioactive properties. The present study determined the potential role of post-extraction ultrasonication on some compositional features and antioxidant properties of resulting extracts after sequential enzymatic/alkaline treatment. No significant difference was detected in terms of protein content and recovery as well as amino acid composition of the extracts. The nitrogen-to-protein conversion factor of 5 was found to be too high for the seaweed and its enzymatic/alkaline extracts. The extracts sonicated by bath for 10 minutes and not sonicated showed the highest and lowest total phenolic contents (p&lt;0.05), respectively. The highest radical scavenging and lowest metal chelating activities were observed for the non-sonicated sample, as evidenced by IC50 values. The extract sonicated by bath for 10 minutes showed the most favorable in vitro antioxidant properties since its radical scavenging was not significantly different from that of not sonicated sample (p&gt;0.05), whereas its metal chelating activity was significantly higher (p&lt;0.05). To conclude, post-extraction ultrasonication by an ultrasonic bath for 10 minutes is recommended to increase phenolic content and improve antioxidant properties of enzymatic-alkaline extracts from P. palmata.