A proposed low-temperature forging method aims to enhance stainless steel bearings by creating a martensitic subsurface layer, significantly boosting bearing fatigue life due to increased surface hardness. This technique induces beneficial residual stresses, particularly in axial bearings, streamlining their construction and improving machine elements. Challenges persist, especially with radial bearings, but simplicity in axial bearing forging promotes compact, resource-efficient facility construction. Future research will focus on applying this technique to axial bearing washers, potentially replicating success in other bearing components. Despite the energy ex-penditure for cooling during forging, the substantial increase in bearing fatigue life offsets this, enhancing overall longevity and reliability of critical machine components. Integration of this forging technique into bearing fabrication appears seamless, offering a promising trade-off be-tween energy use and enhanced performance.

In this work we looked for a stable and accurate procedure to perform feature selection in datasets with a much higher number of predictors than individuals, as in Genome-Wide Association Studies. Due to the instability in feature selection when many potential predictors are measured, a variable selection procedure is proposed that combines several replications of shrinkage regression models. A weighted formulation is used to define the final predictors. The procedure is applied to investigate Single Nucleotide Polymorphisms (SNPs) predictors associated to Alzheimer’s disease in the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset. Two data scenarios are investigated: one considering only the set of SNPs and another with the covariates age, sex, educational level and APOE4 genotype. The SNP rs2075650 and the APOE4 genotype are given as risk factors for AD, which is in line with the literature, and other four new SNPs are pointed, opening new hypotheses for in vivo analyses. These experiments demonstrate the potential of the new method for stable feature selection.

Cercospora beticola Sacc. is an ascomycete pathogen that causes Cercospora leaf spot in sugar beets (Beta vulgaris L.) and other related crops. The disease symptoms are characterized by circular lesions with an ash-gray centre and dark brown to purple margins on the leaves. It can lead to significant yield losses if not effectively managed. This study aims to characterize and assess the impact of bacterial biological control agents against C. beticola. Following a dual-culture screening, 18 bacteria exhibiting over 50% inhibition were selected, with six of them demonstrating more than 80% control. Identification of the bacteria was carried out through sequencing of the16S rDNA, revealing potentially 12 species belonging to six genera including Bacillus which was represented by four species. Additionally, the bacteria underwent in-depth characterization of biochemical and molecular properties, as well as plant-growth promotion. PCR detection of genes responsible for producing antifungal metabolites revealed that 83%, 78%, 89%, and 56% of the selected bacteria possessed the bacillomycin, iturin, fengycin, and surfactin encoding genes, respectively. Infrared spectroscopy analysis confirmed the presence of lipopeptide structure in the bacteria supernatant filtrate. Subsequently, the bacteria were assessed for their effect on sugar beet plants in controlled conditions. The bacteria exhibited notable capabilities, promoting growth in both roots and shoots, resulting in significant increases in root length, weight, and shoot length. The field experiment with four bacterial candidates demonstrated good performance against C. beticola compared to difenoconazole fungicide. These bacteria played a significant role in disease control, achieving a maximum efficacy of 77.42%, slightly below the 88.51% efficacy attained by difenoconazole. Additional field trials are necessary to verify the protective and growth-promoting effects of these candidates, whether applied individually, combined in consortia, or integrated with chemical inputs in sugar beet crop production.

The ongoing challenge to ensure a sustainable and affordable energy supply forces industrial companies to implement appropriate measures. These measures typically lead to an increased system complexity, which has to be addressed during operation. Whereas existing approaches from the field of supervisory and optimal control appear to be capable of mastering this issue, these are still not adopted in industry. Therefore, this work presents a procedure model for the systematic development of application-oriented operating strategies for industrial energy supply systems. The procedure model combines research approaches from the fields of sequencing control and approximate MPC to extract rule-based operating strategies. By splitting the procedure model into five phases, expert knowledge can be integrated in a target-oriented manner. Subsequently to the description, the procedure model is exemplary applied to the example of an industrial heating supply system. Throughout an optimization study, the developed operating strategy is compared to a MPC strategy as well as a baseline strategy. Whereas the conventional MPC approach is the upper bound with regard to optimality, the developed operating strategy is able to generate comparable results. Compared to the baseline strategy, a relative reduction in operating expenses of 5.4 % to 37.0 % are achieved in this specific use case.

This paper presents laboratory devices on which experimental measurements were carried out to prove the validity of the assumption about the reduction of vibrations transmitted to the conveyor belt structure generated by the impact forces of falling material grains in the places of transfer or on the hoppers of conveyor belts. In order to limit damage to the conveyor belts caused by the impact of the sharp edges of material grains, conveyor belts are supported by impact rollers or impact rubber rods in the places of transfers and on the hoppers of belt conveyors. The flattened ends of the impact rollers are inserted into the grooves of steel brackets installed on the supports of the fixed conveyor idlers of a conventional design. In this paper, a special modification of the fixed conveyor idler is presented, which consists of inserting plastic brackets into the structurally modified roller axle holders of the fixed conveyor idler. Measurements were carried out on the first laboratory device, which showed that the specially modified fixed conveyor idler resulted in a higher damping of up to 15% of the impact forces of the falling weight on the rubberized hoop of the impact roller shell compared to the conventional fixed conveyor idler design. Measurements carried out on the second laboratory device show that the effective vibration velocity values detected at the points where the impact roller axis fits into the fixed roller table holder, i.e. points B and D, are higher than when using plastic brackets, up to 6% for a 108 mm diameter roller, compared to steel impact roller brackets.

In mining machines with friction discs, but also in multi-rope traction elevators, it is necessary to distribute the applied tensile load, generated by the weight of the cage and counterweight, evenly in all cross-sections of the load-bearing ropes. Hydraulic devices used for this purpose can operate on the principle of Pascal's law. The article presents a structural design, a 3D model and an implemented solution of a laboratory device capable of simulating a practical method of evenly distributing the total weight of the load into partial tensile forces of the same size acting on a selected number of load-bearing ropes. The laboratory equipment uses two pairs of three steel cables of finite length for the simulations. During the experimental measurements, tensile forces derived from the tractive force of the piston rods, pushed into the bodies of the hydraulic cylinders by the pressure of the hydraulic oil supplied through the pipeline under the pistons of the hydraulic cylinders, were detected. The resulting amount of hydraulic oil pressure in the hydraulic circuit influenced by different values of the hydraulic oil pressures in the hydraulic cylinders and by the pressure in the supply pipe was experimentally studied on the laboratory equipment. Simulations were also carried out in order to detect the hydraulic oil pressure in the hydraulic circuit caused by the change in the different magnitudes of the tensile forces in the ropes From the experiments carried out, it follows that with the appropriate choice of hydraulic elements and the design of the hydraulic circuit, the weight of the load, acting as the total pulling force in the ropes, can be evenly distributed (with a deviation of up to 5%) to all cross-sections of the load-bearing ropes. If the exact values of the hydraulic oil volumes under the pistons of all hydraulic cylinders are not known, it is not possible to calculate the pressure values in the hydraulic circuit when the valves of the hydraulic pipes are gradually opened.

Plant-microbe interactions play a crucial role in shaping plant health and survival. In recent years, the role of extracellular vesicles (EVs) in mediating intercellular communication between plants and microbes has emerged as an intriguing area of research. EVs, serve as important carriers of bioactive molecules and genetic information, facilitating communication between cells and even between different organisms. Pathogenic bacteria leverage extracellular vesicles (EVs) to amplify their virulence, exploiting their cargo rich in toxins and virulence factors. Conversely, beneficial microbes initiate EV secretion to stimulate plant immune responses and nurture symbiotic relationships. The transfer of EV-packed small RNAs (sRNAs) has been demonstrated to facilitate the modulation of immune responses. Furthermore, harnessing the potential of EVs holds promise for the development of innovative diagnostic tools and sustainable crop protection strategies. This review highlights the biogenesis and functions of EVs in bacteria, and their importance in plant defense and paves the way for future research in this exciting field.

Induction heating is a fast, reproducible, and efficient heating method used in various manufacturing processes. However, there is no established additive manufacturing (AM) process based on induction heating using wire as feedstock. This study investigates a novel approach to AM based on inductive heating, where a steel wire is melted and droplets are detached periodically using a two-winding induction coil. The process parameters and energy input into the droplets are characterized. The induction generator exhibits sluggish response to the excitation voltage, resulting in a lag in the coil current. The process is captured using a high-speed camera, revealing regular droplet formation and uniform size and shape when operated within an appropriate process window. Larger drops and increased spatter formation occur outside this window. The proposed method allows for the production of droplets with almost spherical shapes. Further analysis and characterization of droplet formation and energy input provide insights into process optimization and overall efficiency.

To fulfil a need of limitation automotive emissions the reduction of vehicle weight is widely recommended and achieved in many ways, both by the construction of individual elements of the vehicle, and by the selection of light materials including Al alloys. Connecting these elements with each other and with elements made of iron alloys can be realized, inter alia, by welding or stir welding. However, the quality of the welds obtained varies widely and depends on many design, operational and environmental factors. The present study focused on a review of various welding technics used to joint both similar and dissimilar Al alloys utilized in automotive industry, the effect of various process parameters on welds quality, and phenomena appeared in such welds..

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data is made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist’s perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

Background: Developing patient-centric baseline standards that enable the detection of clinically significant outlier gene products on a genome-scale remains an unaddressed challenge required for advancing personalized medicine beyond the small pools of subjects implied by “precision medicine”. This manuscript proposes a novel approach for reference standard development to evaluate the accuracy of single-subject analyses of metabolomes, proteomes, or transcriptomes. Since distributional assumptions of statistical testing may inadequately model genome dynamics of gene products, the so-called significant results of previous studies may artefactually conflate with real signals. Model confirmation biases escalate when studies use the same analytical methods in the discovery sets and reference standards, as corroboration of results leads to an evaluation of reproducibility confounded with replicated biases rather than a measure of accuracy. We hypothesized that developing method-agnostic reference standards using effect-size and expression-level filtering of results, obtained from multiple discovery methods that are distinct from the one evaluated, would maximize the evaluation of clinical-transcriptomic signals and minimize statistical artefactual biases. We developed and released an R package “referenceNof1” to facilitate the construction of robust reference standards. Results: Since RNA-Seq data analysis methods often rely on binomial and negative binomial assumptions to non-parametric analyses, the differences create statistical noise and make the reference standards method dependent. In our experimental design, the accuracy of 30 distinct combinations of fold changes (FC) and expression levels (EL) were determined for five types of RNA analyses in two different datasets. This design was applied to two distinct datasets: breast cancer cell lines and a yeast study with isogenic biological replicates in two experimental conditions. In addition, the reference standard (RS) comprised all RNA analytical methods with the exception of the method testing accuracy. To mitigate for biased optimization of the RS parameters towards a specific analytical method, similarity between observed results of distinct analytical methods were calculated across all methods (Jaccard Concordance Index). The greatest differences were observed across diametric extremes. For example, filtering out differentially expressed genes (DEGs) using a fold change < 1.2 leads to a 50% increase in concordance between techniques when compared to results with FC > 1.2. Combining this FC cutoff with genes with mean expressions > 30 counts leads to a 65% increase in concordance in comparison to genes with expression levels < 30 counts and with FC < 1.2. Conclusions: We have demonstrated that comparing accuracies of different single-subject analysis methods for clinical optimization requires a new evaluation framework. Reliable and robust reference standards, independent of the evaluated method, can be obtained under a limited number of parameter combinations: fold change (FC) ranges thresholds, expression level cutoffs, and exclusion of the tested method from the RS development process. When applying anticonservative reference standard frameworks (e.g., using the same method for RS development and for prediction), a majority of the concordant signal between prediction and Gold Standard (GS) cannot be confirmed by other methods, which we conclude as biased results. Statistical tests to determine DEGs from a single-subject study generate many biased results that require subsequent filtering for increasing their reliability. Conventional single-subject studies pertain to one or a few measures in one patient over time [1]and need a substantial conceptual framework extension in order to address the tens of thousands of measures in genome-wide analyses of gene products. The proposed referenceNof1 framework addresses some of the inherent challenges in improving transcriptome scale single-subject analyses by providing a robust approach to constructing reference standards. Github: https://github.com/SamirRachidZaim/referenceNof1

Oligopeptide transporters (OPTs) are a group of membrane localized proteins that have a broad range of substrate transport capabilities and contribute to numerous biological processes. However, limited information has been reported on OPTs in higher plants. In this study, a comprehensive analysis of the OPT gene family in Medicago truncatula was performed. A total of 26 OPT genes (MtOPT01-MtOPT26) have been identified in the Medicago truncatula genome. Phylogenetic analyses indicated that MtOPTs consisted of two distinct subgroups, 12 MtOPTs belonged to the peptide transport subgroup (PT-OPT) based on their predicted amino acid sequences containing the two highly conserved motifs (NPG and KIPPR) and 14 MtOPTs belonged to yellow stripe subgroup (YS-OPT). The MtOPTs distributed on each of 8 chromosomes in Medicago truncatula. Sequence analysis verified that MtOPTs significant similar to those in other plants. The copy number of MtOPTs was low and the multiply of MtOPTs was simple relatively. Gene structure analysis showed that most of the MtOPTs have various numbers of introns. The multiple of MtOPTs might play different biological roles which were supported by the fact that MtOPTs have a distinct tissue-specific expression pattern. The data obtained in this study will help to better understand the complexity of the MtOPTs gene family and provide new evidence for the function and evolution of the OPT gene family in higher plants.

Background: Alfalfa, the most economically important forage legume worldwide, features modest genetic progress due to long selection cycles and large extent of non-additive genetic variance associated with its autotetraploid genome. Methods: To improve the efficiency of genomic selection in alfalfa, we explored the effects of genome parametrization (as autotetraploid and diploid dosage, plus allele ratios) and SNP marker subsetting (all available SNPs; only genic regions; only non-genic regions) on genomic regressions, together with various levels of filtering on reading depth and missing rates. We used Genotyping by Sequencing-generated data and focused on traits of different genetic complexity, i.e., dry biomass yield in moisture-favorable (FE) and drought stress (SE) environments, leaf size, and onset of flowering, assessed on 143 genotyped plants from a genetically-broad European reference population and their phenotyped half-sib progenies. Results: On average, allele ratio improved the predictive ability compared with other genome parametrizations (+7.9% vs. tetraploid dosage, +12.6% vs. diploid dosage), SNP subsetting offered an advantage compared with any tested subsettings (+3.7% vs. genic regions, +7.6% vs. non-genic regions). However, when focusing on specific traits, different combinations of genome parametrization and subsetting reached better performances. We also released Legpipe2, a SNP calling pipeline tailored for reduced representation (GBS, RAD) medium-sized genotyping experiments.

This study aimed to determine shifts in microbial populations regarding richness and diversity from the daily use of a popular over-the-counter nasal spray. In addition, the finding of nasal commensal bacterial species that overlap with the oral microbiome may prove to be potential probiotics for the “gateway microbiomes.” Nasal swab samples were obtained before and after using the most popular over-the-counter (OTC) nasal spray in 10 participants aged 18–48. All participants were healthy volunteers with no significant medical histories. The participants were randomly assigned a number by randomizing software and consisted of five men and five women. The sampling consisted of placing a nasal swab atraumatically into the nasal cavity. The samples were preserved and sent to Northwestern University Sequencing Center for whole-genome deep sequencing. After 21 days of OTC nasal spray use twice daily, the participants returned for further nasal microbiome sampling. The microbial analysis included all bacteria, archaea, viruses, molds, and yeasts via deep sequencing for species analysis. The Northwestern University Sequencing Center utilized artificial intelligence analysis to determine shifts in species and strains following nasal spray use that resulted in changes in diversity and richness.

With 500+ papers and 20+ books spanning many scientific disciplines, Mark Burgin has left an indelible mark and legacy for future explorers of human thought and information technology professionals. In this paper, I discuss his contribution to the evolution of machine intelligence using his general theory of information (GTI) based on my discussions with him and various papers I co-authored during the past eight years. His construction of a new class of digital automata to overcome the barrier posed by the Church-Turing Thesis, and his contribution to Super-Symbolic Computing with Knowledge Structures, Cognizing Oracles, and Structural Machines are leading to practical applications changing the future landscape of information systems. GTI provides a model for the operational knowledge of biological systems to build, operate, and manage life processes using 30+ trillion cells capable of replication and metabolism. The schema, and associated operations derived from GTI are also used to model a digital genome specifying the operational knowledge of algorithms executing the software life processes with specific purposes using replication and metabolism. The result is a digital software system with a super-symbolic computing structure exhibiting autopoietic and cognitive behaviors that biological systems also exhibit. We discuss here one of these applications.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

The goal of the paper is to clarify the observed irrationality of decision making in conflict situations considered as one-step games of two players. To solve such situations, we consider the asymmetry in the relation of the players to their own rewards and the rewards of the opponents. Formalization of the decision-making process is based on recently developed non-commutative operators of multivalued logic algebra. The suggested method is applied to solve the well-known Prisoners’ dilemma game and the other situations of conflict, where it results in the expected strategies.

Scientists can use the Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) protein 9 system, which is a powerful and flexible tool for changing the genome, to change only certain parts of the genome. This has helped researchers figure out how target genes work in biology and disease. CRISPR/Cas9 is made up of a sequence-specific CRISPR RNA (crRNA) set and a generic Cas9 nuclease. The crRNA set tells Cas9 where to cut DNA and cause double-strand breaks. In the next step of cellular DNA repair, insertions, deletions, and swaps are made at specific spots. For CRISPR/Cas9-mediated DNA cleavage to be specific, the target sequences must match the crRNA, and a protospacer nearby motif must be placed after the target sequences. In this article, we talk about how CRISPR/Cas9 works at the molecular level, what it can be used for, what it can't do, and what its future clinical treatment promise might be.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating Photoplethysmogram (PPG) and Electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls. Feature selection techniques were used to systematically select key features which were identified for their clinical relevance and significance in capturing cardiovascular dynamics and to reduce computational complexity and to decrease the chance of overfitting the ML algorithms. These features are then utilized to train and evaluate machine learning algorithms, resulting in a model with an impressive accuracy of 98%, sensitivity of 97.60%, specificity of 96.90%, and precision of 97.20%. The integrated approach outperforms single-signal strategies, showcas-ng its potential for early, precise, and non-invasive heart failure diagnosis. Furthermore, the study underscores the significance of continuous monitoring through wearables, emphasizing the benefits of integrating multiple signals for a comprehensive evaluation of cardiovascular health. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Genetic engineering has become an essential element in developing climate-resilient crops and environmental sustainable solutions to respond to the increasing need for global food security. Genome editing using CRISPR/Cas [Clustered regulatory interspaced short palindromic repeat (CRISPR)-associated protein (Cas)] technology is being applied to a variety of organisms, including plants. This technique has become popular because of its high specificity, effectiveness, and low production cost. Therefore, this technology has the potential to revolutionize agriculture and contribute to global food security. Over the past few years, increasing efforts have been seen in its application in developing higher-yielding, nutrition rich, disease resistant, stress-tolerant “crops” fruits and vegetables. Cas proteins such as Cas9, Cas12, Cas13, and Cas14, among others, have distinct architectures and have been used to create new genetic tools that improve features that are important for agriculture. The versatility of Cas has accelerated genomic analysis and facilitated the use of CRISPR/Cas to manipulate and alter nucleic acid sequences in cells of different organisms. This review provides the evolution of CRISPR technology exploring its mechanisms and contrasting it with traditional breeding and transgenic approaches to improve different stress tolerance. We have also discussed the CRISPR/Cas system and explored three Cas proteins that are currently known to exist: Cas12, Cas13, and Cas14 and their potential to generate foreign DNA free or non-transgenic crops that could be easily regulated for commercialization in most countries.

Diacylglycerol kinase (DGK) is a lipid kinase that phosphorylates diacylglycerol (DAG) to generate phosphatidic acid (PA). Based on converting one important signaling molecule (DAG) to another (PA), DGK plays an important role in plant responses to abiotic stress including waterlogging stress. However, no studies have been reported on the characterization of the DGK gene family in the waterlogging-tolerant kiwifruit germplasm, Actinidia valvata Dunn. Here, we identified 18 AvDGK genes in the A. valvata genome. The phylogenetic analysis showed that AvDGKs can be classified into three clusters, and members within the same cluster have similar domain distribution, exon-intron structures and conserved motif compositions. Chromosome localization analysis revealed that all the AvDGK genes are located across 18 different chromosomes. There were 29 duplicated gene pairs in kiwifruit and all had undergone purifying selection during evolution. Promoter cis-element analysis revealed that the cis-elements within AvDGK genes are associated with multiple functions, including phytohormone signal transduction, stress response, and plant growth and development. The expression pattern analyses indicated that AvDGK play an important role in the fruit development and plant response to waterlogging stress. AvDGKs gene family in the tetraploid A. valvata genome might promote PA synthesis and subsequent signal transduction both under short-term and long-term waterlogging stress. These results provide information regarding the structural characteristics and potential function of DGK genes within kiwifruit and lay a fundamental basis for further research into breeding for enhancing the kiwifruit’s tolerance to waterlogging stress.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

Personal protective equipment (P PE) rules must be respected and followed to the letter of healthcare professionals in Cameroon to avoid the transmission of infectious diseases. This research proposes solutions to the problems of non-adherence and non-compliance among healthcare workers in Cameroon towards PPE rules. Inadequate access to suitable PPE, discomfort when wearing PPE during poor quality, and a lack of training on correct PPE use are just a few of these obstacles amongst HCWs in Cameroon. Hospitals and healthcare facilities should prioritize offering thorough instruction on PPE use and pinpoint the need to adhere consistently to PPE rules to remove these obstacles. They should also ensure that all healthcare personnel have access to and a sufficient supply of the proper PPE. Through the organization of impromptu visits by surveillance teams to identify areas of support and training, there is a need for assistance from government bodies and public health groups in creating awareness of the security value of PPE use among healthcare professionals. There is a lack of PPE in health care settings in rural areas in Cameroon, thus a need for an adequate supply. Then can we put appropriate PPE policies and practices for HCWs in Cameroon, as they will keep to it.Healthcare professionals in Cameroon must comprehend that following PPE standards protects not just their selves from diseases but also their patients, coworkers, and communities. We are accountable for ensuring everyone's safety and well-being, and adhering to PPE regulations is an essential first step.

In this paper, a novel permanent magnet-assisted synchronous reluctance machine (PMASynRM) with rare-earth PMs and ferrite magnets is proposed. The performance of PMASynRM is discussed with respected to the different magnet ratio of rare-earth PMs and ferrite magnets. Some characteristics including the flux density, output torque, cogging torque, output power, power factor, torque ripple, loss, efficiency, and demagnetization are calculated by 2-D finite element analysis (FEA). The analysis results show that the excellent performance can be obtained by using hybrid magnet of rare-earth PMs and ferrite magnets with the suitable magnet ratio, and provide some desirable cost-performance trade-off.

The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.

The dynamic behaviour of a machine tool (MT) directly influences the machining performances. The adoption of lightweight structures may reduce the effects of undesired vibrations and increasing the workpiece quality. This paper aims to present and compare a set of hybrid materials that may be excellent candidate to fabricate the MT moving parts. The selected materials have high dynamic characteristics and capacity to damp mechanical vibrations. In this way, starting by the kinematic model of a milling machine that highlights the main critical factors, this study paper evaluates a number of prototypes made of Al Foam sandwiches (AFS), Al Corrugated sandwiches (ACS) and materials reinforced by carbon fibres (CFRP). A set of prototypes has been fabricated, represented the Z-axis ram of a commercial milling machine. The static and dynamical properties have been evaluated by using both FE simulations and experimental tests. The obtained results show that the proposed structures may be a valid alternative to the conventional materials of MT moving parts, increasing machining performances. In particular, the AFS prototype highlighted a damping coefficient that is 20 times greater than a conventional ram (e.g. steel). The CFRP structure is able to satisfy the machining requirements with a reduced weight of 48.5%, while the ACS prototype showed a good trade-off between stiffness and damping.

IgA nephropathy (IgAN) is a kidney disease that is characterized by the deposition of IgA (immunoglobulin A) in the kidney tissue, leading to inflammation and damage. This disease is a common chronic glomerulopathy that affects people worldwide (1). Therefore, it is crucial to identify factors that worsen the disease, monitor its activity, and predict appropriate treatments. Complement component 4d (C4d) is a protein that is involved in the activation of the complement system, which is a part of the immune system.

Wearable computing is a fast evolving segment of computing that includes smart watches, head mounted wearables such as Magic Leap headsets, Microsoft Hololens, and VR goggles from various vendors. In this report, we present ideas for a smart wearable device that also doubles as a virus protection device. Instead of using the filtering approach that is predominantly used by virus protection equipments such as face masks, we propose to use a computational approach where the device maintains an awareness of the real-time virus spread and use that information to steer the wearer away from the virus. As the wearable has a head enclosing design, viral infection can only happen through the air that is inhaled by the wearer. The objective of the smart wearable is to maintain a repository for clean air and switch the operating modes between stored and fresh air modes depending on the environmental conditions. It can augment this basic operating procedure by recycling the exhaled air to maximize it operating capacity (i.e., time duration for which it could supply the wearer with safe air) and by cleaning the stored air using UVC to further reduces the chance of infection. To maintain an awareness of the virus spread in the environment, the smart wearable will rely on an edge computing framework that will be distributed to cover areas frequented by people. The smart wearable will have modular design so that it can be reconfigured to add or subtract functionality that the wearer wants for a particular situation so that the design remains relevant even after the virus threat recedes.

Gender Identity Dysphoria (GID) is a condition characterized by a strong and persistent identification with the opposite sex. These people consider themselves victims of a sort of biological accident: "a soul in a wrong body". There are numerous theories on the origin of transsexualism: genetic, hormonal and psychological causes have been hypothesized, but those currently most accredited are the neuroanatomical ones. The cornerstones of hormone conversion therapy (Gender Affirming Hormone Therapy, GAHT) are feminizing hormones for transgender women (MtFs) and virilizing for transgender males (FtMs). GID can be present among adolescents and older people. For adolescents is now accepted reversible treatment of puberty withdrawal with hormones that stops the progression of pubertal development in the biological direction not accepted; for elderly people is suggested GAHT in reduced doses. Physicians should consider and discuss with people with GID about fertility preservation, general and cancer risks. We present also data of 127 transsexual patients enrolled at the Garibaldi-Nesima Andrology Clinic in Catania (Italy) from 2003 to 2020. To optimize the conversion treatment with sex hormones, transsexuals require long-term follow-up. GAHT must be performed by a doctor who is familiar with these problems. Therefore, the “do-it-yourself” trend and the lack of medical and laboratory checks over time should be absolutely discouraged. Before proceeding with the surgical sex reassignment, it is recommended to refer to an endocrinologist and psychologist or psychiatrist for a period of 2-3 years. The transition surgical conclusion process must be practiced by a quality surgical team.

Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.

To make wind power more competitive, it is necessary to reduce turbine downtime and reduce costs associated with wind turbine Operation and Maintenance (O&amp;M). Incorporating machine learning in developing condition-based predictive maintenance methodologies for wind turbines can enhance their efficiency and reliability. This paper presents a monitoring method that utilizes Base Density for the Support Vector Machine (BDSVM) and the evolutionary Fourier spectra of vibrations. This method allows smart monitoring of the function evolution of the turbine. A complex optimal function (FO) for 5-degree order has been developed that will be the boundary function of the BDSVM to timely determine the magnitude, frequency, and place of the failure occurring in wind turbine drive.

Use of a multi-sensor approach can provide citizens a holistic insight in the air quality in their immediate surroundings and assessment of personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from 7 European cities, discusses data fusion and harmonization on a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants, and offers possible solutions and improvements. Harmonizing the data streams identified issues with the used devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate the process of generating visualizations and reports and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization on a diverse set of multi-sensor data streams considerably improved the quality and quantity of data that a research participant receives. Though automatization accelerated the production of the reports considerably, manual structured double checks are strongly recommended.

Gender-based violence is considered a serious social and public health problem. Overcoming this situation implies a process that results in the favorable biopsychosocial rehabilitation, the resilient of women. The objective of this study was to analyze the tools, resources and personal and psychosocial mechanisms used by women survivors of gender-based violence. The design was an interpretative phenomenology. It carried out with 22 women who have overcome gender-based violence. Data was collected through personal interviews and narration. The results were grouped into four themes: "Process of violence", "Social resources for coping and overcoming GBV", "Personal tools for coping and overcoming GBV", and "Feelings identified, from the abuse stage to the survival stage". Several studies concluded that overcoming abuse is influenced by the women social network, and it can be the action of these people determining their survival to gender violence. Despite the recognized usefulness of these available resources, it would be desirable to strengthen them in order to be able to drive more women toward survival, assuming a strengthening of coping and overcoming, without forgetting the importance of other support mechanisms such as their family and group therapies.

Personal health data is acquired, processed, stored, and accessed using a variety of different devices, applications, and services. These are often complex and highly connected. Therefore, use or misuse of the data is hard to detect for people, if they are not capable to understand the trace (i.e., the provenance) of that data. We present a visualization technique for personal health data provenance using comics strips. Each strip of the comic represents a certain activity, such as entering data using a smartphone application, storing or retrieving data on a cloud service, or generating a diagram from the data. The comic strips are generated automatically using recorded provenance graphs. The easy-to-understand comics enable all people to notice crucial points regarding their data such as, for example, privacy violations.

Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

Detection of subsurface archaeological remains using a range of remote sensing methods, poses several challenges until today. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near infrared part of the spectrum, were taken over a controlled environment in Cyprus during the 2011-2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, and in particular in the neighborhood of 570 nm, and (2) the red edge-near infrared part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on, in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors' knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.

Analysis of land use/land cover (LULC) in the catchment areas is the first action toward safeguarding the freshwater resources. The LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), such as Random Forests (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Naïve Bayes (NB) was investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs were based on the visual inspection of the analyst and the commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and kappa coefficient. The MLAs produced good results, where RF (OA= 97.02%, Kappa= 0.96), SVM (OA= 89.74 %, Kappa= 0.88), ANN (OA= 87%, Kappa= 0.86), and NB (OA= 68.64 Kappa= 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a small margin. While NB yielded satisfactory results, which could be primarily influenced by its sensitivity to limited training samples. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases.

Tapping panel dryness (TPD) syndrome is a complex disease of Rubber tree (Hevea brasiliensis L.) which causes cessation of latex drainage upon tapping of rubber tree. Rubber tree virus (RTV1) was identified as a novel pathogen associated with rubber tree and a potential causal agent of TPD. RTV1 is a monopartite RNA virus that is linear, non-enveloped and has a single-stranded (ss) positive RNA genome of approximately 6081 nucleotides and is composed of two major open reading frames (ORFs), ORF1 (polyprotein), and ORF2 (movement protein. This study aimed to investigate the possibility of rubber genome encoded tree microRNAs (miRNAs) as novel therapeutic targets against RTV1 using in silico algorithms. Mature rubber tree miRNAs are retrieved from the miRBase database and are used for hybridization of RTV1 using five different five different computational algorithms including miRanda, RNA22, RNAhybrid and psRNATarget. A total of eleven common rubber tree miRNAs were identified based on consensus genomic positions. The consensus of four algorithms predicted the hybridization sites of hbr-miR396a and hbr-miR398 at common locus positions 6676, 1840 respectively. To validate the prediction, secondary structures of the consensual rubber tree miRNAs and free energy of duplex binding were calculated using the RNAfold and RNAcofold algorithms respectively. We created a plot between rubber tree miRNAs and RTV1 ORFs by using Circos algorithm. In this study, we predicted eleven consensual rubber tree miRNAs. Among these miRNAs, hbr-miR398 was identified as the most effectual miRNA that may target the ORF1 gene of the RTV1 genome. The predicted data will be important in the development of rubber trees resistant to RTV1.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

Structures inevitably suffer damages after an earthquake, with their severity ranging from minimal damages to non-structural elements to partial or even total collapse, possibly with loss of human lives. Thus, it is essential for engineers to understand the crucial factors that drive a structure towards suffering higher degrees of damage, so that preventative measures can be taken. In the present study, we focus on three well-known damage thresholds, namely Collapse Limit State, Ultimate Limit State and Serviceability Limit State and analyze which of the features obtained via Rapid Visual Screening, determine whether or not a given structure will cross that threshold. To this end, we use Machine Learning to perform binary classification for each damage threshold, as well as Explainability via SHAP values (acronym for SHapley Additive exPlanations) to quantify the effect of each parameter. The quantitative results obtained demonstrate the potential applicability of ML methods to re-calibrate the computation of structural vulnerability indices using data from recent earthquakes.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

One of the special trends in health care is the increasing availability of data and services to the cloud, especially for convenience (for example, providing a complete patient medical record without interruption) and savings (for example, economic issues). Management of health care data). However, there are limitations to using common cryptographic prototypes and access control models to address security and privacy concerns in an increasingly cloudy environment. In this paper, we explore the potential and capacity of using China's Blockchain technology to protect health care data hosted in the cloud. We also explain the real challenges of such an approach and further research is needed. Health care is a highly data-dependent domain, with large amounts of data being created, published, stored and accessed daily. For example, data are created when a patient undergoes a number of examinations (such as computed tomography or computed tomography scans) and the data need to be sent to the radiologist and then to a physician. The visit results are then stored in the hospital, and then need to be accessed later by another physician at another hospital within the network. It is clear that technology can play an important role in improving the quality of care for patients (for example, using data analytics to make informed medical decisions) and potentially costing more by allocating resources more efficiently in terms of personnel, equipment, etc. , Reduce. For example, paper-based data extraction is difficult to extract into systems (for example, it is costly and may involve data entry errors), archiving them and accessing them as needed is costly. These challenges may cause medical decisions to be incomplete, requiring repeated tests for missing information or missing data stored in another hospital in another state or country (at the expense of increased costs and no convenience). (For patients) and so on. Because of the nature of the industry, it is important to ensure the security, privacy and integrity of health care data. As a result, there is definitely a need for a secure and secure data management system.

Low-cost personal exposure monitors (PEMs) to measure personal exposure to air pollution are potentially promising tools for health research. However, their adoption requires robust validation. This study evaluated the performance of twenty-one Plume Lab Flow2 (PLF) by comparing its air pollutant measurements, particulate matter with a diameter of 2.5 μm or less (PM2.5), 10 μm or less (PM10), and nitrogen dioxide (NO2), against several high-quality air pollution monitors under field conditions (at indoor, outdoor, and roadside locations). Correlation and regression analysis were used to evaluate measurements obtained by different PLFs against reference instrumentation. For all measured pollutants, the overall correlation coefficient between the PLFs and the reference instruments was often weak (r<0.4). Moderate correlation was observed for one PLF unit at indoor location and two units at roadside location, when measuring PM2.5, but not for PM10 and NO2 concentration. During periods of particularly higher pollution, 11 PLF tools showed stronger regression results (R2 values > 0.5) with one-hour and 9 PLF units with one-minute time interval. Results show that the PLF cannot be used robustly to determine high and low exposure to poor air. Therefore, the use of PLFs in research studies should be approached with caution if data quality is important to the research outputs.

With the abundance of conversations happening everywhere, dialogue summarization plays an increasingly important role in the real world. However, dialogues inevitably involve lots of personal pronouns, which hinder the performance of existing dialogue summarization models. This work proposes a framework named WHORU to inject external personal pronoun resolution (PPR) information into abstractive dialogue summarization models. To reduce time and space consumption, we further propose a simple and effective PPR method for the dialogue domain. Experiments demonstrated the superiority of the proposed methods. More importantly, we achieve new SOTA results on SAMSum and AMI datasets.

Digital twins are an emerging technology that could be harnessed for the digitalization of the industry. Steel industry systems contain a large number of electro-hydraulic components, as proportional valves. An input-output model for a novel water proportional cartridge valve is derived from physical modelling based on fluid mechanics, dynamics, and electrical principles. The valve is a two-stage valve with two 2/2-way water proportional valves as the pilot stage and a marginally stable poppet-type cartridge valve as the main valve. The orifice equation, which is based on Bernoulli principles, is approximated by a polynomial which makes parameter estimation easier and makes modelling possible without measuring pressure of the varying control volume situated in the pilot stage part of the valve. Data were collected when the valve was operating as a component in a closed-loop system as part of a press-mill machine in a steel manufacturing plant. Parameter estimation was made by convex optimization with physical constraints to overcome problems caused by poor system excitation. For comparison, a simple linear model was derived and the least squares method used for parameter estimation. A thorough estimation of the parameters relative errors is presented. The model contains 5 parameters related to the design parameters of the valve. The modelled position output is in good agreement with experimental data for training and test data. The model can be used for real time monitoring of the valve’s status by the model parameters. One of the model parameters varies linearly with production cycles. Thus, aging of the valve can be monitored.

Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. This article provides an overview of count data and count models, explores zero inflation, introduces Likelihood Ratio Tests, and explains how the Vuong Test can be used as a model selection criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.

Aim: Streptococcus pneumoniae and influenza H1N1 virus are common organisms associated with human infections. These infections could play a significant role in immune regulation. The study was performed to analyse the genome sequences of these organisms with human genome and study its functional significance. Methods: The study was performed to analyse the overlapping of genome sequences in Streptococcus pneumoniae and Influenza (H1N1) virus against human genome sequences by BLASTn sequence analysis. The alignments are studied against the corresponding genes for their functional significance with DAVID and NDEx software.Results: Several hits or overlapping nucleotide segments were identified. Between streptococcus and homo sapiens 287 overlaps were identified, and among influenza and homo sapiens 124 hits were identified. A wide range of functional significance of these genes were identified, and the results are presented in this study. The results show insights into functional pathways and biological activities associated with the respective vaccinations or infections by these microorganisms. Conclusion: The common organisms like Streptococcus pneumoniae and Influenza H1N1 virus actively interact with the immune system and result in a wide range of immune regulations.

Structural deformations are one of the most significant factor that affects machine tool (MT) positioning accuracy. These induced errors are complex to be represented by a model, nevertheless they need to be evaluated and predicted in order to increase the machining performance. This paper presents a novel approach to calibrate a machine tool in real-time, analyzing the thermo-mechanical errors through Fibre Bragg Grating (FBG) sensors embedded in the MT frame. The proposed configuration consists of an adaptronic structure of passive materials, Carbon Fibre Reinforced Polymers (CFRP), equipped by FBG sensors that are able to measure in real-time the deformed conditions of the frame. By using a proper thermo-mechanical kinematic model, the displacement of the end effector may be predicted and corrected when it is subjected to external undesired factors. By starting from a set of FE simulations to develop a model able to describe the MT structure stresses, a prototype has been fabricated and tested. The scope was to compare the numerical model with the experimental tests using FBG sensors. The experimental campaign has been performed varying the structure temperature over time and measuring the tool tip point (TTP) positions. The obtained results showed a substantial matching between the real and the predicted position of TTP confirming the effectiveness of the proposed calibration system.

Opossums, native to the Americas, are unique marsupials with over 100 species. The Common Opossum and Virginia Opossum are notable species. They are nocturnal, omnivorous, and adapt to various habitats. Their reproduction involves a short gestation and marsupial pouch development. Despite their ecological benefits like seed dispersal and insect control, their proximity to human habitats exposes them to pathogens, posing public health risks. These include protozoa, helminths, and arthropods that can spread zoonotic parasites and vector-borne diseases. The marsupial immune system, including opossums, is complex, rivaling eutherian mammals. Key immune genes were identified in the grey short-tailed opossum genome. CD1 protein and VpreB surrogate light chain were also found. Marsupials are proposed as ideal models for studying developmental immunology. A West Indian opossum (Didelphis marsupialis) was found and examined. Blood tests revealed abnormalities, including a low white blood cell count and signs of anemia. The results contribute to understanding the species’ health and adaptability in the wild.

Bacteriophages, viruses that are widespread throughout the world, are highly specific for bacteria, usually of a single species and often of a particular strain. After being discovered and isolated 100 years ago, their use, called phage therapy, was instituted in medicine two years later and quickly used around the world to treat various bacterial infections. In the West, phage therapy was overshadowed in the second half of the 20th century by antibiotic therapy, which was then thought to be the definitive solution. But because of the increase in bacterial resistance to antibiotics, the idea of using bacteriophages in medicine has been reawakened. The innumerable observations reported over the years in the literature constitute an invaluable experience. We and some of our colleagues have, in the last decade treated some patients compassionately. With the available documentation and our own experience we discuss the potential indications and limitations of phage therapy. The observation of the increasing number of therapeutic failures in the announced perspective of a post-antibiotic era, we believe, that the introduction of bacteriophages into the therapeutic arsenal seems conceivable today to two preconditions: that their production as biologic drug meets current regulatory standards and that the benefit-risk assessment was conducted in a modern setting. Phage therapy could be applied as a substitution or supplement to antibiotic therapy under multiple circumstances in different modes, precise indications and limits.

Modular steel buildings (MSBs) interlocking inter-module connections (IMCs) have piqued researchers’ interest. However, the existing literature has not yet studied the optimisation of its plate thicknesses. This paper, therefore, focuses on optimising interlocking (IMCs) plate thickness in (MSBs), leveraging previous experimental and numerical simulation methodologies. Various numerical models for four MSBs interlocking (IMCs) with plate thicknesses (4 mm, 6 mm, 10 mm, and 12 mm) were developed under compression loading conditions. The study’s novelty lay in its comprehensive parametric analysis, which evaluated the slip phenomenon in (MSBs) connections and introduced a slip prediction model validated against empirical data. Further innovative machine learning approach was utilised to predict slip values based on applied force through a random forest regression model trained using the 'Treebagger' function. Sensitivity analysis and comparisons with alternative methods were utilised to underscore the reliability and applicability of the findings. The results showed that 11.03 mm was the optimal plate thickness for interlocking (IMCs) in modular steel buildings, with up to 8.08% reduced material costs. Increasing plate thickness boosts its deformation resistance by up to 50.75%. ‘TreeBagger’ random forest for anomaly detection and machine learning improves slip prediction up to 7% at higher force levels.

This essay is a personal insight based on my own experience in the Iberian Pyrenees, which addresses three situations common in paleoecological research, such as the verification of previously devised hypotheses (anticipation), the finding on unknown events in unstudied sites (discovery) and the finding of unexpected outputs in already known areas (serendipity). The account is concentrated on the value of the coring sites by themselves as generators of paleoecological knowledge, rather than on the actual findings, which are presented and discussed in the corresponding data papers. The main aim is to show that there is still much room for new findings, even in areas that have been surveyed for long time and are supposed to be well known, from a paleoecological perspective. Finally, some general lessons are derived and conceptualized.

Abstract: The present study seeks to investigate MNOs leadership style and how it influences their compassion competence and their personal level of compassion at work. This is a cross-sectional study carried out from December 2019 to May 2020 using the method of convenience sampling. The study involved 235 MNOs serving in Greek Military Hospitals. A single questionnaire containing Compassion at Work index, Compassion competence scale, and Multifactor Leadership Questionnaire (MLQ-5X), and socio-demographic and professional data, was used for data collection. A total of 400 printed questionnaires were distributed with a response rate of 58.75%. Data analysis was performed using the statistical package SPSS 22.0. The research showed that the transformational and transactional leadership styles coexist in the Nursing Corps of the Armed Forces with an average value of 2.72(SD=0.70)-2.95(SD=0.54) points and 2.47(SD=0.69)-2.74(SD=0.63) points respectively, while the passive style represented a very small percentage with an average subscale value of 0.88(SD=0.61)-0.94(SD=0.63) points. It was also found that both actual compassion at work and compassion ability had improved with the increase of transformational or transactional leadership style characteristics and amelioration of leadership outcome criteria. On the other side, a deterioration of these was observed with the increase of the passive leadership. Specifically, a higher score in the «Intellectual Stimulation» scale was associated with a higher level of compassion at work in the dimension «Experiencing the suffering of others» (p=0.010/SD=0.14), while higher values on the «Laissez-Faire Leadership» scale were associated with less compassion at work in the same dimension (p<0.001/SD=0.13). Also, a higher score on the «Contingent Reward» scale was associated with more compassion at work in the dimension «Takes appropriate action» (p=0.023/SD=0.16). Furthermore, higher values observed in the «Inspirational Motivation», «Individual Consideration» and «Extra Effort» scales were associated with a better communication ability (p=0.035/SD=0.09, p=0.022/SD=0.12, and p=0.042/SD=0.08 accordingly). Finally, a higher score on the «Effectiveness» scale was associated with higher sensitivity (p=0.049/SD=0.08). Teaching appropriate leadership behavior, promoting a culture of compassion, and continuing to train nurses to manage their emotions should be included in the infrastructure of nursing science.

Reciprocal Personal/Public Protection (RPPP) featured with source control is introduced, Mask Discriminating Mouth and Nose (MDMN) is employed to serve the purpose, which includes polymer based mouth cover with optional nose cover. The new knowledge that mouth is a primary, active and dominant source of the virus has been well established, which is the base of MDMN. Source classification and related source control tools are discussed, mouth cover is recommended as the tool prioritized to use. Liquid droplets is identified as a hard issue related to mask, liquid droplets, mask fitting, comfort and facial recognition constitute real challenges of mask in addition to efficiency, All of these have been addressed with MDMN. Comparisons between MDMN and masks/face covering are taken on four aspects: efficiency and efficacy, tolerance and comfort, cost and waste, and civil rights and public interest. Mouth cover is recommended to replace the face covering and act as both a personal tool and a public utensil, mouth cover with nose cover can provide better protection than N95 etc. RPPP with MDMN, could be an alternative for lockdown, a parallel strategy to vaccine, and a collectively living way during the pandemic era. MDMN, featured with high efficiency protection, high degree comfort, easy wearing, tight-fitting, easy facial recognition & communication, reusability, cost-effective, environment friendly and scale manufacturing more readily and widely etc., is a simple and sustainable solution, which is essential for ordinary people to keep wearing it properly for protection.

Resuscitation is one of the most stressful tasks in emergency medicine. The participation of medical professionals in this procedure can have specific effects on them. In the research, we wanted to find out what these effects are. A qualitative approach by conducting semi-structured interviews was used, and thematic data analysis of the recorded interviews was done. The collected data were transcribed verbatim with no corrections to the audio recordings. The computer program ATLAS.ti 22 was used for qualitative data analysis. Eleven male registered nurses were interviewed, with an average of 18.5 years of experience working in a prehospital environment (max. 32/min. 9). A total of 404 minutes of recordings were analyzed and 789 codes were found, which were combined into 36 patterns, and 11 themes. As the most stressful situa-tions, the interviewees pointed out the resuscitation of a child, familiar persons, conflicts with the environment, conflicts within the resuscitation team, non-functioning or insufficient equipment, complications during resuscitation, and resuscitating a person only for exercise. As positive effects, the interviewees cited successful resuscitations or their awareness that, despite the unsuccessful resuscitation, they did everything they could. Participation in these interven-tions has a specific positive or negative impact on the performers. The interviewees shared the opinion that they can cope effectively with resuscitation's adverse or stressful effects. Yet, de-spite everything, they allow the possibility of subconscious influences of this intervention on them.

The exposure to extreme temperatures in workplaces involves physical hazards for workers. A poorly acclimated worker may have lower performance and vigilance and may therefore be more exposed to accidents and injuries. Due to the incompatibility of the existing standards implemented in some workplaces and the lack of thermoregulation in many protective equipment, thermal stress remains one of the most frequent physical hazards in many work sectors. However, many of these problems can be overcome with the use of smart textile technologies that enable intelligent thermoregulation in personal protective equipment. Smart textiles can detect, react and adapt to many external stimuli. Interconnected sensors and actuators that interact and react to existing risks can provide the wearer with increased safety, protection and comfort. Thus, the skills of smart protective equipment can contribute to the reduction of errors and the number and severity of accidents in the workplace, and thus promote improved performance, efficiency and productivity.This review provides an overview and opinions of authors on the current state of knowledge on these types of technologies by reviewing and discussing the state of the art of commercially available systems and the advances made in previous research works.

Malaria is a serious infection affecting millions of people in Africa. Our study investigated the personal preferences and applications of antimalarial medicines in Ghana. Based on over 1000 questionnaires distributed in Ghana from January to May 2019, we noticed that although Western medications to fight this disease are widely available, most patients in Ghana prefer treatment with locally produced herbal remedies. This preference appears to be due to a combination of traditional venues for obtaining medicines “on the street” rather than in licensed pharmacies, trust in local and “green” products, extensive advertisement of such local products, and an inherent distrust of imported and synthetic or unnatural medicines. Going local and natural is a trend also observed in other countries across the globe, and adds to the acceptance or rejection of drugs regardless of their activity or toxicity. In fact, adverse side effects associated with herbal remedies, such as general weakness and swollen, sore mouth, do not seem to deter the respondents of this study in Ghana. We propose a combination of (a) increasing public awareness of the benefits of modern medicine and (b) an improvement and control of the quality of herbal remedies to raise the standard of malaria treatment in countries such as Ghana.

Although food retailers have embraced organic certified food products as a way to reduce their environmental loading, organic sales only make up a small proportion of total sales worldwide. Most consumers have positive attitudes towards organic food, but attitudes are not reflected in behaviour. This article addresses consumers’ attitude-behaviour gap regarding their purchase organic food and reports on how visualization of personal shopping data may encourage them to buy more organic food. Through the design of the visualization tool, the EcoPanel, and through an empirical study of its use, we provide evidence on the potential of the tool to promote sustainable food shopping practices. Sixty-five users tested the EcoPanel for five months and interviews were made with ten of these. The test users increased their purchase of organic food with 23%. The informants used the EcoPanel to reflect on their shopping behaviour and to increase their organic shopping. We conclude that the visualization of food purchases stimulates critical reflection and the formation of new food shopping practices. This implies that food retailers may increase sales of organic food through using a visualization tool available for their customers. In this way these retailers may decrease their environmental impact.

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

To fully appreciate genetics, one must understand the link between genotype (DNA sequence) and phenotype (observable characteristics). Advances in high-throughput genomic sequencing technologies and applications, so-called “-omics”, have made genetic sequencing readily available across fields in biology from applications in non-traditional study organisms to precision medicine. Thus, understanding these tools is critical for any biologist, especially those early in their career. This comprehensive review discusses the chronological development of different sequencing methods, the bioinformatics steps to analyzing this data, and social and ethical issues raised by these techniques that must be discussed and evaluated.

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

Adolescence is a stage of change and transition, which together with the COVID-19 situation, has been a huge source of vulnerability and facilitated the development of emotional disorders. This study aims to analyse internalising (anxiety and depression) and externalising (anger and aggression) symptoms in adolescence according to gender, as well as personal resources (self-esteem, social competence and integration, and awareness of problems) put in place as a mechanism of protection and coping after the situation of the pandemic. The study population encompassed 445 adolescents, of whom 230 were boys and 215 were girls, between the ages of 12 and 17. A self-reported Likert-type test was used. Findings indicate that girls show greater levels of internalising (anxiety and depression) and externalising problems (anger), finding no differences for the variable aggression between the two groups. Personal resources are closely linked to emotional symptomatology, providing a source of protection in both genders. The findings indicate a maladaptive pattern for adolescents that requires early intervention within the field of education.

We develop new theory from a broad case-study approach to explore a better understanding of what constitutes knowledge and its value as identifiable to human-machine teams. From past research, this trail of exploration will lead to these initial questions: What is the value of debate in the furtherance of knowledge? Will machines with AI be able to contribute to a debate if we humans cannot define it or determine its value sufficiently for a machine’s understanding, contribution, exploration and identification? Like humans, as teammates, machines must be able to determine with AI what constitutes the usable knowledge that contributes to a team’s success in the field (e.g., testing "knowledge" in the field, identifying new knowledge, using knowledge to develop innovation) or its failure (viz., trouble shooting; identifying weaknesses; discovering vulnerabilities, hiding by deception). It matters not whether a debate is public, private or unexpressed by an individual human or machine agent alone; we speculate in this exploration that the process advances the science of autonomous human-machine teams and assists in interpretable machine learning. We conclude with questions and a speculation: How does a human become aware or express its awareness of knowledge? Can a machine be as expressive as its human teammates? And how does a human-machine teammate become aware that its teammates possess sufficient knowledge to perform a task? We speculate that the structure of “knowledge," once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its applications are multifaceted (akin to maximum entropy production). The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric.

Abstract: The study used whole-genome sequencing (WGS) and bioinformatics analysis for the genomic characterization of 60 isolates of Listeria monocytogenes isolated from cattle farms, cattle abattoirs, and retail outlets in Gauteng province, South Africa. The isolates' sequence types (STs), clonal complexes (CCs), and lineages were determined using in silico multilocus sequence typing (MLST). We used BLAST-based analyses to identify virulence and antimicrobial genes, plasmids, proviruses, and the CRISPR-Cas system. The study investigated any association of the detected genes to the origin in the beef production chain of the L. monocytogenes isolates. Overall, in 60 isolates of Listeria monocytogenes, there were 7 STs, 6 CCs, 44 putative virulence factors, 2 resistance genes, 1 plasmid with AMR genes and 3 with conjugative genes, 1 CRISPR gene, and all 60 isolates were positive for proviruses. Among the 7 STs detected, ST204 (46.7%) and ST2 (21.7%) were the most prominent, with ST frequency varying significantly (p&lt;0.001). The predominant CC detected were CC2 (21.7%) and CC204 (46.7%) in lineages I and II, respectively. Of the 44 virulence factors detected, 26 (across Listeria Pathogenicity Islands, LIPIs) were present in all the isolates. The difference in the detection frequency varied significantly (P&lt;0.001). The two AMR genes (fosX and vga(G)) detected were present in all 60 (100%) isolates of L. monocytogenes. The only plasmid, NF033156, was present in 3 (5%) isolates. A CRISPR-Cas system was detected in 6 (10%), and all the isolates carried proviruses. Significant differences were detected in the frequencies of STs and virulence factors regarding the source and sample type of the L. monocytogenes isolates. The presence of both fosX and vga(G) genes in all the isolates from the three industries (cattle farms, abattoirs, and retail outlets) can potentially cause therapeutic implications. Our study, which characterized L. monocytogenes recovered from the three levels in the beef production chain in the country, provides the first evidence of the distribution of the pathogen with potential food safety and therapeutic implications.

The goal was to determine the personal, interpersonal, and sociocultural predictors of condom use among rural indigenous adolescents. Predictor variables were selected from Bandura's Social Cognitive Theory and Leininger's Transcultural Theory. The sample consisted of 419 Nahuas adolescents randomly selected from the total number of neighborhood blocks in a rural community in Puebla, Mexico. The instruments had acceptable psychometric characteristics (Cronbach alpha and validity scores). Multiple linear regression models were used. Results: 56.8% of participants were female, and 50.40 % were students. The mean age was M = 17.5 (SD = 0.97), and the majority (63%) identified as Catholic. Age at menarche/first ejaculation (β = -1.2, p = .038), attitude toward condom use (β = .13, p < .001), ethnic identity (β = .21, p < .001), and ability to negotiate condom use (β = .13, p < .003), predicted (R2 = 22.3) condom use. This study provides a basis for the integration of the cultural values of indigenous adolescents within interventions for sexual health promotion.

Road vehicles are a large contributor to Nitrogen Oxides (NOx) pollution. The routine road-side monitoring stations, however, may underrepresent the severity of personal exposure in urban areas, because long-term average readings cannot capture the effects of momentary, high peaks of air pollution. While numerical modelling tools historically have been used to propose an improved distribution of monitoring stations, ultra-high resolution Computational Fluid Dynamics models can further assist the relevant stakeholders in understanding the important details of pollutant dispersion and exposure at local level. This study deploys a 10 cm-resolution CFD model to evaluate actual high peaks of personal exposure to NOx from traffic, by tracking the gases emitted from the tailpipe of moving vehicles being dispersed towards the roadside. The investigation shows that a set of four Euro 5-rated diesel vehicles travelling at constant speed may generate momentary road-side concentrations of NOx as high as 1.25 mg/m3, with 25% expected increase for doubling the number of vehicles and approximately 50% reduction when considering Euro 6-rated vehicles. The paper demonstrates how the numerical tool can be used to identify the impact of measures to reduce personal exposure, such as protective urban furniture, as traffic patterns and environmental conditions change.

Facing the tasks and challenges going forward, a pilot of the Indonesian Naval Aviation Center was required to be brave in taking risks, especially in carrying out its main tasks, so it was expected that a pilot of the Indonesian Naval Aviation Center had intelligence, especially in finding appropriate solutions and alternatives to address every task challenges dynamics in situations that often changed quickly. The purpose of this study was to identify the personal character and performance that should be owned by the pilot of the Indonesian Naval Aviation Center. This research used a qualitative approach with a contextual and constructional meta-analysis study method. The results of this meta-analysis study concluded that in line with the essence of personal character that is defined as the personal aspects of a work that allows workers to achieve superior performance. These personal aspects include nature, emotive-motives, value systems, attitudes, knowledge, and skills, where the personal character would produce an optimal performance on a pilot of the Indonesian Naval Aviation Center.

The blood counts of α thalassemia carriers (α-thal) are similar to those of β thalassemia carriers, except for Hgb A2 that is not elevated. The objective of this study was to determine whether mathematical formulas are effective for detecting suspected α-thal. The data were obtained from the database of the prevention program for detecting couples at risk for having a child with hemoglobinopathy. RBC indices were analyzed using mathematical formulas and the sensitivity and negative predictive value (NPV) calculated. Among 1334 blood counts suspected α-thal analyzed, only the Shine&Lal and the Support Vector Machine formulas revealed high sensitivity and NPV. Sensitivity was 85.54 and 99.33% and NPV 98.93 and 99.93%, respectively. Molecular defects were found in 291, 81 had normal α genes. Molecular analysis was not performed in 962. Based on these results, mathematical formulas incorporating one of these reliable formulas for detecting suspected α or β thalassemia carriers in the program of the automatic analyzers can flag these results and increase the awareness of the primary physicians about the carrier risk and send an alert with a recommendation for further testing.

This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions of Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, and clinical variables impact patient perceptions of service quality, influencing their healthcare choices. Through the analysis of mobility trends with machine learning, significant differences between regions were uncovered, highlighting the influence of regional context on perceived quality. The integration of SHAP (SHapley Additive exPlanations) values into our analysis provided deeper insights into the logistic regression model, elucidating the specific contribution of each variable to perceived healthcare quality. This incorporation of SHAP values underscores the study's commitment to employing advanced, explainable AI techniques to enhance the interpretability and fairness of healthcare service evaluations. The choice of logistic regression elucidated the impact of specific variables on quality perception, offering essential insights for optimizing healthcare resource distribution and underscoring the importance of data-driven strategies to foster more equitable, efficient, and patient-centred healthcare systems. Contributing to the understanding of perceived quality dynamics within the healthcare context, the research paves the way for further investigations into enhancing accessibility and service quality, leveraging machine learning as a tool for improving healthcare services efficiency in diverse regional settings.

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a simplified RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

The number of AI applications in education is growing every day. One recent AI application in the educational sector is Chatbot technology, which is used to support teaching and administrative tasks. This document presents the design and implementation of a Chatbot called Tashi-Bot that helps applicants and university students to obtain information from an educational institution about certain academic and administrative processes. Among these are processes related to well-being, tuition, costs, admission, and other services. In order to design the Chatbot, an analysis of the state of the art, methodologies, and suitable tools was carried out, and a survey was conducted to discover the needs of users and their preferences in the use of a Chatbot for this specific purpose. Tashi-Bot was implemented on the SnatchBot platform and later deployed on a Telegram channel. In its evaluation, a final survey was carried out to check on the satisfaction of the users. The results suggest that Tashi-Bot could help applicants and university students to find information on academic and administrative processes with great certainty and without the need for human interaction. Tashi-Bot can be found at: https://web.telegram.org/#/im?p=@TashiE_Bot..

The modern era of polarimetric radar begins with radiowave propagation research starting in the early 1970s with applications to measurement and modeling of wave attenuation in rain and depolarization due to ice particles along satellite-earth links. While there is a rich history of radar in meteorology after World War II, the impetus provided by radiowave propagation requirements lead to high quality antennas and feeds. Our journey starts by describing the key institutions and personnel responsible for development of weather radar polarimetry. The early period was dominated by circularly polarized radars for propagation research and at S-band for hail detection. By the mid-to late 70s, a paradigm shift occurred which led to the dominance of linear polarizations with applications to slant path attenuation prediction as well as estimation of rain rates and inferences of precipitation physics. The period from early 1980s to 1995 can be considered as the “golden” period of rapid research that brought in meteorologists, cloud physicists and hydrologists. This article describes the evolution of this technology from the vantage point of the authors. Their personal reflections and “behind the scenes” descriptions offer a glimpse into the inner workings at several key institutions which cannot be found elsewhere.

This survey paper discusses the Cointegrated VAR methodology and how it has evolved over the last 30 years. The first section is a description of major steps in the econometric development of the CVAR model that facilitated serious real world applications. The next three sections are primarily methodological and discuss (i) difficulties and puzzles when confronting theory with the data, (ii) the formulation of a viable link between theory and the data, a so called theory-consistent CVAR scenario, and (iii) how all this was inspired by Trygve Haavelmo and his Nobel prize winning monograph "The Probability Approach to Economics". The next two sections discuss early applications of the Cointegrated VAR model to monetary transmission mechanisms, international transmission mechanisms and wage, price and unemployment dynamics. They report puzzling evidence, discuss the need for new theory, and propose a method for combining partial CVAR analyses into a larger macroeconomic model. The following sections propose a new, empirically-based, approach to macroeconomics in which imperfect knowledge based expectations replace so called rational expectations and in which the financial sector plays a key role for understanding the long persistent movements in the data. The last section argues that the CVAR can act as a "design of experiment for passive observations" and illustrates with several applications including unemployment dynamics under crises periods and aid effectiveness in South Saharan African countries.

Whole genome alignment (WGA) is a critical process in comparative genomics, facilitating the detection of genetic variants and aiding our understanding of evolution. This paper offers a detailed overview and categorization of WGA techniques, encompassing suffix tree-based, anchors-based, and graph-based methods. It elaborates on the algorithmic properties of these tools, focusing on performance and methodological aspects. The paper underscores the latest progress in WGA, emphasizing the increasing capacity to manage the growing intricacy and volume of genomic data. However, the field still grapples with computational and biological hurdles affecting the precision and speed of WGA. We explored these challenges and potential future solutions. This paper aims to provide a comprehensive resource for researchers, deepening our understanding of WGA tools and their applications, constraints, and prospects.

Recently, there have been revolutions in the development of both gene therapy and genome surgical treatments for inherited diseases. Much of this progress has been centered around hereditary retinal dystrophies, because the eye is an immune-privileged and anatomically ideal target. Gene therapy treatments, already demonstrated to be safe and efficacious in numerous clinical trials, are benefitting from the development of new viral vectors, such as dual and triple AAVs. CRISPR/Ca9, which revolutionized the field of gene editing, is being adapted into more precise “high fidelity” and catalytically dead variants. New CRISPR endonucleases, such as CjCas9 and Cas12a, are generating excitement in the field as well. Stem cell therapy has emerged as a promising alternative, allowing human embryo derived stem cells and induced pluripotent stem cells to be edited precisely in vitro and then reintroduced into the body. This article highlights recent progress made in gene therapy and genome surgery for retinal disorders, and it provides an update on precision medicine FDA treatment trials.

The growing shift toward Internet of Things (IoT)-based solutions expands the attack surface of systems by connecting an extensive network of heterogeneous devices and technologies. The heterogeneity of IoT devices and the scale of the network often make conventional security measures impractical. Therefore, recent research efforts have focused on machine learning (ML)-based device-agnostic IoT security solutions. However, most proposed solutions have focused solely on detecting malicious traffic in IoT networks. While this is important, further information about the attack is needed to provide a comprehensive defense before, during, and after the incident. To address this gap, we proposed an anomaly-based intrusion detection model that detects malicious traffic and identifies the attack category and subcategory with high accuracy, recall, precision, and F1-score. The proposed intrusion detection model is an ensemble model that integrates multiple ML models to produce more robust and reliable detections. For evaluation, we performed three sets of experiments: (1) a binary classification to detect malicious traffic; (2) a multi-class classification to detect the attack category; and (3) a multi-class classification to detect the attack subcategory. In the experiments, our model achieved an accuracy, recall, precision, and F1-score of 100% for the binary detection; a precision, recall, and F1-score of 99% and an accuracy of 100% for the multi-class category classification; and an accuracy, recall, precision, and F1-score of 88% for the multi-class subcategory classification. These results suggest that the proposed model can reliably detect anomalies in IoT devices.

Preconception lifestyle modification and reducing several known risk factors may have an influence on the future pregnancy outcome. The aim of the study was to analyse health behaviour and personal values, as well as to assess the relationship of these factors in women without children, in pregnant ones and in women who had already delivered babies. The questionnaire survey included Health Behaviour Inventory (HBI), Personal Value List (PVL) and the sociodemographic data and was conducted in 538 women. These women were divided into 3 groups: women who recently delivered (n = 235), pregnant women (n = 121) and childless women (n = 182). Pregnant women demonstrated a significantly higher level of declared health behaviours and also they rated higher subscales values "positive mental attitude" and "health practices" in comparison to women who recently delivered and to childless women. In all tested groups the highest rated personal value was "a successful family life", while the most appreciated symbol of happiness was "love and friendship". Our results suggest that the system of values and the perception of happiness symbols may influence women's health behaviours. Positioning “health” in the hierarchy of personal values as the most important one may facilitate introduction of healthy behaviours. This in turn could reduce several adverse pregnancy outcomes that are potentially modifiable with changing preconception health attitudes. Our results also identify several unanswered questions and highlights areas where new research is needed.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

Nowadays, the development of civilization requires a vision of balancing the interests of employees and employers in the sphere of work as never before. Work-life balance is directly linked to social sustainability. The aim of this article is to analyse various dimensions of mutual enrichment of the professional and private life of an individual and to describe how positive experiences in professional and non-professional life influence the improvement of satisfaction, health and achievements, thus enabling the sustainable development of the individual. The conducted research was of a qualitative nature. Thematic exploration was used to analyse the findings of 34 in-depth interviews with experienced HR managers and employees at various levels of enterprises in Poland. The research shows that the work and personal life of the respondents interact, complement, and enrich in different ways, depending on the stage of the employee’s life. Habits developed by practicing a specific sport discipline or other type of hobby are helpful in the effective implementation of professional tasks. Also, non-professional interests, including communing with culture and art have a positive impact on professional activities. On the other hand, the respondents emphasized that thanks to their professional activities, specific to the type of work they perform, they are sometimes more extroverted, meticulous, organized and consistent when performing activities outside of work and in other aspects of private life.

The objective of the present study is to isolate phages targeting multidrug resistant (MDR), extended spectrum beta lactamase (ESBL) positive clinical isolate of E. coli ( U1007), sequence and analyze the phage genome and use machine learning tools to predict host cell surface receptor and finally evaluate the efficiency of monophage and phage cocktail in vitro and in vivo in a zebrafish model. Phage specific for E. coli U1007 was isolated from Ganges River (designated as U1G), Cuoom River (designated as CR) and Hospital waste water (designated as M phage). The obtained phages were triple purified and enriched. U1G phages had a greater burst size of 124 PFU/cell and a latent time of 25 min.M phage had a burst size of 150 PFU/cell with a shortlatent time of 20min. Similarly CR phage has a short latent 20 min and a burst size of 115 PFU/cell. Based on capsid architecture, U1G phage resembles Podoviridae, CR phage is structurally similar to Myoviridae and M phage has morphology that resembles Siphoviridae. Genome sequencing and analysis revealed that the size of the U1G phage genome is 73275 bp Whereas that of CR phage and M phage are 45236 bp and 45294 bp, respectively. All three genomes were marked by the absence of genes encoding tRNA sequence, antibiotic resistant or virulent genes. A machine learning (ML) based multi-class classification model using algorithms such as Random Forest, Logistic Regression, and Decision Tree were employed to predict the host receptor targeted by all 3 phages and the best performing algorithm Random Forest predicted LPS O antigen , LamB or OmpC for U1G FhuA, OmpC for CR phage and FhuA, LamB, TonB or OmpF for the M phage as the host receptor targeted by the receptor binding protein (RBP) of the phages. In vitro time kill assay showed that treatment with monophages alone and along with colistin resulted in regrowth whereas phage combinations significantly reduced the regrowth and by 24h the phage cocktail along with colistin produced a significant 3 log declinein cell counts relative to the untreated control. In vivo intramuscular infection study in zebrafish showed that phages were non toxic and a cocktail of dual (U1G +M) phage along with colsitin resulted in a significant 3.5 log decline in cell counts whereas triple phage combination along with colistin resulted in 3 log decline in cell counts probably due to host receptor competition. Our study highlights the potential of phage cocktail therapy in mitigating MDR clinical isolate of E. coli in vitro and in vivo.

With an ever increasing amount of research data available, it becomes constantly more important to possess data literacy skills to benefit from this valuable resource. An integrative course was developed to teach students the fundamentals of data literacy through an engaging genome sequencing project. Each cohort of students performed planning of the experiment, DNA extraction, nanopore sequencing, genome sequence assembly, prediction of genes in the assembled sequence, and assignment of functional annotation terms to predicted genes. Students learned how to communicate science through writing a protocol in the form of a scientific paper, providing comments during a peer-review process, and presenting their findings as part of an international symposium. Many students enjoyed the opportunity to own a project and to work towards a meaningful objective.

The aim was to implement a value-promoting program (Teaching Personal and Social Responsibility, TPSR) and to assess its impact on students. Method: The program was applied for 8 months with 257 students from three secondary school centres (151 boys and 106 girls) with a mean of 15.97 years old (SD = 2.31). They were in three groups, 67 students (control group), 90 students receiving at least 60% of the total teaching time the value-promoting program (experimental group for global education, EG-GE) and 100 students (experimental group only for physical education, EG-PE). As main results improvements were found in the EG-GE for responsibility, psychological mediator index, self-determination index, resilience, climate and prosocial behaviours. In the EG-PE improved were observed in the self-determination index, classroom climate and prosocial behaviours. Female students and from EG-GE improve much more than males and from EG-PE. The outcomes in psychological variables can be higher if TPSR is applied to the whole subjects apart from physical education. These results are even more pronounced for female students in personal and social responsibility. It is worth highlighting the importance of coordinating educational institutions to facilitate the involvement of the greatest number of teachers.

The COVID-19 crisis has resulted in a shortage of personal protective equipment (PPE) [1]. COVID-19 is currently the leading cause of death in the United States[2]. Health care providers caring for COVID-19 patients or at high risk of being exposed to the SARS-CoV-2 virus benefit from a face shield to protect against aerosol droplets that could hit the face and minimize the chance of inadvertently touching the face with contaminated hands, and air filtration to filter out aerosolized SARS-CoV-2. Adapting commercially available full-faced snorkel masks has been proposed as an alternative to narrow the gap in PPE [3]. Here we explore a full-faced snorkel mask with commercially available particulate filters.

Snakes are a vital component of wildlife resources and are widely distributed across the globe. Bungarus multicinctus, a highly venomous snake, is found in central and southern China. B. multicinctus is a highly venomous snake and is distributed in central and southern China. Snakes are an ancient group of reptiles, and their genome resources can provide important clues for understanding the evolutionary history of reptiles. Meanwhile, genomic resources play a crucial role in comprehending the evolution of species. So far, the genomic resources of snakes are a rarity. In 2021, a snake sample was collected from Beiliu Longgukeng, Guangxi, which was identified as B. multicinctus through morphological identification. In this study, we present a highly contiguous genome of B. multicinctus with a size of 1.51 Gb. The genome contains a repeat content of 40.15%, with a total length exceeding 620 Mb. Additionally, we annotated a total of 24,869 functional genes. This research is of great significance for comprehending the evolution of B. multicinctus and provides a genomic basis for the genes involved in venom gland function.

Enterovirus A71 (EV-A71) infection is a major cause of hand, foot and mouth disease (HFMD) which may be occasionally associated with severe neurological complications. There are currently a lack of treatment options for EV-A71 infection. The Raf-MEK-ERK signaling pathway, in addition to its critical importance in the regulation of cell growth, differentiation and survival, has been shown to be essential for virus replication. In this study, we investigated the anti-EV-A71 activity of vemurafenib, a clinically approved B-Raf inhibitor used in the treatment of late stage melanoma. Vemurafenib exhibits potent anti-EV-A71 effect in cytopathic effect inhibition and viral load reduction assays, with half maximal effective concentration (EC50) at nanomolar concentration. Mechanistically, vemurafenib interrupts both EV-A71 genome replication and assembly. These findings expand the list of potential antiviral candidates of anti-EV-A71 therapeutics.

The nucleosome is a major modulator of DNA accessibility to other cellular factors. Nucleosome positioning has a critical importance in regulating cell processes such as transcription, replication, recombination or DNA repair. The DNA sequence is a major factor influencing the position of nucleosomes on genomes. Different sequence motifs can promote or inhibit the nucleosome formation, thus influencing the accessibility to the DNA. Sequence-encoded nucleosome positioning having functional consequences on cell processes can then be selected or counter-selected during evolution. We review the interplay between sequence evolution and nucleosome positioning evolution. We first focus on the different ways to encode nucleosome positions in the DNA sequence, and to which extent these mechanisms are responsible of genome-wide nucleosome positioning in vivo. Then, we discuss the findings about selection of sequences for their nucleosomal properties. Finally, we illustrate how the nucleosome can directly influence sequence evolution through its interactions with DNA damage and repair mechanisms. This review aims to provide an overview of the mutual influence of sequence evolution and nucleosome positioning evolution, possibly leading to complex evolutionary dynamics.

Cardiomyopathies are diseases of heart muscle, a significant percentage of which are genetic in origin. Cardiomyopathies can be classified as dilated, hypertrophic, restrictive, arrhythmogenic right ventricular or left ventricular non-compaction, although mixed morphologies are possible. A subset of neuromuscular disorders, notably Duchenne and Becker muscular dystrophies, are also characterized by cardiomyopathy aside from skeletal myopathy. The global burden of cardiomyopathies is certainly high, necessitating further research and novel therapies. Genome editing tools, which include zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeats (CRISPR) systems have emerged as increasingly important technologies in studying this group of cardiovascular disorders. In this review, we discuss the applications of genome editing in the understanding and treatment of cardiomyopathy. We also describe recent advances in genome editing that may help improve these applications, and some future prospects for genome editing in cardiomyopathy treatment.

As the COVID-19 pandemic begins to abate and national public health systems are treating the SARS-Cov-2 virus as endemic, many public health measures are no longer mandated, but remain recommended with voluntary participation. One of these is the wearing of fitted face masks, initially mandated to contain, or at least slow, the spread of SARS-CoV-2 which is primarily transmitted via aerosols emitted while breathing, coughing, or sneezing. While the habit of once wearing fitted face masks recedes into memory for much of the population, so does the knowledge of the various types of masks that were once en vogue. To create a record for the future, this paper provides the first comprehensive documentation of the nature and range of fitted facemasks that circulated during the COVID-19 pandemic.

Corruption is a legal and moral contravention that generates great concern among nations due to its erosive effects on economic growth, democracy, governance, social equity and the integrity of culture. Therefore, it is necessary to know more about the multiple variables that produce and maintain this phenomenon. This article reports the results of an experimental investigation that sought to provide elements of judgment on the influence of the structure of personal values ​​on the willingness to bribe. For this, 12 couples of young university students of both sexes interacted through a game that simulated a relationship between a corrupter and a corruptible agent, with the help of a real-time communication system, mediated by an online platform, which allowed instant and bidirectional interaction. The couples were randomly constituted and played with each other under double blind. However, the interacting members of the dyad could share values ​​or vary in their axiological structure in such a way that, while one of them could manifest anti-corruption principles, the other's commitment was not so obvious. The results confirm the importance of the axiological structure of people in the facilitation or containment of the corrupt behavior. This article also reflects on the theoretical connotations of the relationships shown.

Parkinson’s disease (PD) is characterized by motor deficits and a wide variety of non-motor symptoms. The age of onset, rate of disease progression and the precise profile of motor and non-motor symptoms display considerable individual variation. Neuropathologically, the loss of substantia nigra dopaminergic neurons is a key feature of PD. The vast majority of PD patients exhibit alpha-synuclein aggregates in several brain regions, but there is also great variability in the neuropathology between individuals. While the dopamine replacement therapies can reduce motor symptoms, current therapies do not modify the disease progression. Numerous clinical trials using a wide variety of approaches have failed to achieve disease modification. It has been suggested that the heterogeneity of PD is a major contributing factor to the failure of disease modification trials, and that it is unlikely that a single treatment will be effective in all patients. Precision medicine, using drugs designed to target the pathophysiology in a manner that is specific to each individual with PD, has been suggested as a way forward. PD patients can be stratified according to whether they carry one of the risk variants associated with elevated PD risk. In this review we assess current clinical trials targeting two enzymes, leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA), which are encoded by two most common PD risk genes. Because the details of the pathogenic processes coupled to the different LRRK2and GBA risk variants are not fully understood, we ask if these precision medicine-based intervention strategies will prove “precise“ or “personalized“ enough to modify the disease process in PD patients. We also consider at what phases of the disease that such strategies might be effective, in light of the genes being primarily associated with the risk of developing disease in the first place, and less clearly linked to the rate of disease progression. Finally, we critically evaluate the notion that therapies targeting LRRK2 and GBA might be relevant to a wider segment of PD patients, beyond those that actually carry risk variants of these genes.

Human footprint is having a unique set of ridges unmatched by any other human being, and therefore it can be used in different identity documents for example birth certificate, Indian biometric identification system AADHAR card, driving license, PAN card, and passport. There are many instances of the crime scene where an accused must walk around and left the footwear impressions as well as barefoot prints and therefore it is very crucial to recovering the footprints to identify the criminals. Footprint-based biometric is a considerably newer technique for personal identification. Fingerprints, retina, iris and face recognition are the methods most useful for attendance record of the person. This time world is facing the problem of global terrorism. It is challenging to identify the terrorist because they are living as regular as the citizens do. Their soft target includes the industries of special interests such as defense, silicon and nanotechnology chip manufacturing units, pharmacy sectors. They pretend themselves as religious persons, so temples and other holy places, even in markets is in their targets. These are the places where one can obtain their footprints easily. The gait itself is sufficient to predict the behaviour of the suspects. The present research is driven to identify the usefulness of footprint and gait as an alternative to personal identification.

Sorghum (Sorghum bicolor L.) ranks fifth as the most crucial cereal crop globally, yet its seed morphology remains relatively unexplored. This study investigated seed morphology in sorghum based on 115 mini core and 130 Senegalese germplasms. Eight seed morphology traits encompassing size, shape, and color parameters were assessed. Statistical analyses explored potential associations between these traits and resistance to three major sorghum diseases: anthracnose, head smut, and downy mildew. Furthermore, genome-wide association studies (GWAS) were conducted using phenotypic data from over 24,000 seeds and over 290,000 publicly available single nucleotide polymorphisms (SNPs) through the Genome Association and Prediction Integrated Tool (GAPIT) R package. Significant SNPs associated with various seed morphology traits were identified and mapped onto the reference sorghum genome to identify novel candidate defense genes.

This paper presents a study of the 2D roughness profiles on a flat surface generated on a steel work piece, by ball nose end milling, with linear equidistant tool paths (pick-intervals). The milled surface exploration with a surface roughness tester (on pick and feed directions) produces usually 2D roughness profiles having periodical evolutions. These evolutions can be seen as time-dependent signals, describable as a sum of sinusoidal components (the wavelength of each component being regarded as a period). In order to detect a well approximated description of these components, two appropriate signal processing techniques are used in this work: first technique provides a direct mathematical (analytical) description and is based on computer aided curve (signal) fitting (more accurate), the second technique (providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform (less accurate). This study can be seen as a way for a better understanding the interaction between the tool and the work piece or to achieve a mathematical description of the machined surface microgeometry in terms of roughness (e. g. its description being a collection of closely spaced 2D roughness profiles).

A signal analysis of the genoma sequenced of coronavirus variants: B.1.1.7, B.1.135, B.1.429-B.1.427, B.1.525 and P1 is presented. We deal with a certain type of finite alternating sum series having independently distributed terms associated with binary (0,1) indicators for the nucleotide bases A,C,G,T. This method provides additional information to conventional Similarity comparisons and Power Spectrum approaches. It leads to uncover distinctive patterns regarding the intrinsic data organization of genomic sequences according to its progression along the nucleotide bases position. Hence, the method could be useful for survelliance of genoma variants.

Despite advances in algorithms and computational platforms, de-novo genome assembly remains a challenging process. Due to the constant innovation in sequencing technologies (Sanger, SOLiD, Illumina, 454, PacBio and Oxford Nanopore), genome assembly has evolved to respond to the changes in input data type. This paper includes a broad and comparative review of the most recent short-read, long-read and hybrid assembly techniques. In this review, we provide (1) an algorithmic description of the important processes in the workflow that introduces fundamental concepts and improvements; (2) a review of existing software that explains possible options for genome assembly; and (3) a comparison of the accuracy and the performance of existing methods executed on the same computer using the same processing capabilities and using the same set of real and synthetic datasets. Such evaluation allows a fair and precise comparison of accuracy in all aspects. As a result, this paper identifies both the strengths and weaknesses of each method. This comparative review is unique in providing a detailed comparison of a broad spectrum of cutting-edge algorithms and methods.

This paper presents the development of a novel anti-violence device titled "no pAIn" (an acronym for Never Oppressed Protected by Artificial Intelligence Nonviolence system), which harnesses the power of artificial intelligence (AI). Primarily designed to combat violence against women, the device offers personal safety benefits for individuals across diverse demographics. Operating autonomously, it necessitates no user interaction post-activation. The AI engine conducts real-time speech recognition and effectively discerns genuine instances of aggression from non-violent disputes or conversations. Facilitated by its Internet connectivity, in the event of detected aggression, the device promptly issues assistance requests with real-time precise geolocation tracking to predetermined recipients, for immediate assistance. Its compact size enables discreet concealment within commonplace items like candy wrappers, or within purpose-built casings, or as wearable accessories. The device is battery-operated. The prototype was developed using a microcontroller board (Arduino Nano RP2040 Connect), incorporating an omnidirectional microphone and Wi-Fi module, all at a remarkably low cost. Subsequent functionality testing, performed in debug mode using the Arduino IDE serial monitor, yielded successful results. The AI engine exhibited exceptional accuracy in word recognition, complemented by a robust logic implementation, rendering the device highly reliable in discerning genuine instances of aggression from non-violent scenarios

Transformative learning is an emerging and unique learning theory for adults. Foreign adult education scholars, represented by Mezirow, have produced significant research results in this field. However, research in this area in China is relatively lacking. This paper interprets the personal story of an English teacher through six stages and combines transformative learning theory to position the roles of adult learners in transformative learning. The author believes that adult learners today must take on six missions and play the roles of discerners, reflectors, communicators, integrators, aspirers, and resilient individuals in the transformative learning process.