Lyme disease, the leading vector-borne ailment in the U.S., annually affects an estimated 476,000 individuals, predominantly in the Northeast and Upper Midwest. Despite its increasing incidence, the evaluation of risk within U.S. cities, including natural public lands, remains inadequate. This study focuses on blacklegged tick occurrences and Borrelia burgdorferi infection prevalence in 24 Staten Island parks, aiming to assess Lyme disease exposure risk. Monthly acarological risk index (ARI) calculations from 2019 to 2022 revealed elevated values (0.16-0.53) in specific parks, notably Wolfe’s Pond Park, High Rock Park, Clay Pit Pond Park, Clove Lake Park, and Fair View Park. June (0.36) and November (0.21) consistently exhibited heightened ARIs, aligning with peak tick collection months. Despite stable yearly infection rates at 28.97%, tick densities varied significantly between parks and years. Identifying a high transmission risk in specific parks in Staten Island, a highly urbanized part of New York City, emphasizes the continuous necessity for Lyme disease risk management, even within the greenspaces of large cities.

In this paper, we investigate the so-called electronic dressed states, a unified quasiparticle resulting from the interaction between electrons in a two-dimensional material with an off-resonance optical dressing field. If the frequency of this field is much larger than all characteristic energies in the system, such as the Fermi energy or bandgap(s), the electronic band structure is affected by radiation so that some important properties of the electron dispersions could be modified in a way desirable for practical applications. For example, a circularly polarized light can be used to vary the bandgap of Dirac materials: it opens a gap in graphene and other metallic and semimetallic lattices, or modifies the magnitude of an existing gap. This will either enhance or reduce a gap, depending on its initial value as well as properties of a host material. Here, we consider gaped dice and Lieb lattices as samples, and put forward a full theoretical model to reveal how these electronic states are deformed by an elliptically-polarized irradiation with a focus on generation and modification of a bandgap.

The oral mucosa represents a defense between the external environment and the rest of the body and encompasses several antimicrobial and antiviral mechanisms. Oral mucosal cells are constantly bathed in hypotonic saliva (normally one-third tonicity compared to plasma) and are repeatedly exposed to environmental stresses of tonicity, temperature and pH by the drinks we imbibe (e.g. hypotonic: water, tea, coffee; hypertonic: assorted fruit juices, red wines), as well as of diverse microbial and viral flora. In the mouth, the broad-spectrum antiviral mediator MxA (a dynamin-family large GTPase) is constitutively expressed in healthy periodontal tissues, and also induced by Type III interferons (e.g. IFN-λ1). Endogenously induced human MxA and exogenously expressed human GFP-MxA formed membraneless biomolecular condensates in the cytoplasm of oral carcinoma cells (OECM1 cell line). These condensates likely represent storage granules in equilibrium with antivirally active dispersed MxA. Remarkably, cytoplasmic MxA condensates were exquisitely sensitive sensors of hypotonicity – the condensates in oral epithelium disassembled within 1-2 min of exposure of cells to saliva-like one-third hypotonicity, and spontaneously reassembled in the next 4-7 min. OECM1 cells preloaded with calcein-AM (a reporter of cytosolic “macromolecular crowding”) confirmed unquenching and then requenching of calcein fluorescence during this process. Hypertonicity had little effect on MxA condensates. The spontaneous reassembly of GFP-MxA condensates in oral epithelial cells even under continuous saliva-like hypotonicity was slowed by the protein phosphatase inhibitor cyclosporin A (CsA) and by the K-channel blocker tetraethylammonium chloride (TEA) suggestive of the involvement of the volume-sensitive WNK kinase-protein phosphatase (PTP)-K-Cl cotransporter (KCC) pathway in the regulated volume decrease (RVD) during condensate reassembly in oral cells. Thus, physiologically, hypotonic drinks (water, tea, coffee) trigger a rapid deployment of antiviral MxA throughout the cytoplasm of oral epithelial cells, with subsequent salvage of MxA back into storage granules/condensates.

We bring a strength-based and socially-oriented lens to behaviors observed in profound autism. Social deficits are a defining characteristic of autism. Yet humans’ biological drive for social connection is sufficiently strong that is should be observable, in some form at least, in individuals with autism. We describe a case that validates this view. Mahad is a 20-year-old Pakistani-American male, with intellectual disability, and minimal verbal abilities. Mahad has an intense interest in food and the steps involved in food preparation. He employs and capitalizes on his family’s meal routines to ensure continual social attention from his mother. When not immersed in food, Mahad uses his limited capacities to pursue the additional biological needs of exploring his environment, seeking information, and developing his cognitive abilities.

The emergence of the Internet of Things (loT) has led to remarkable advancements in various sectors such as urbanization, manufacturing, and healthcare, intending to enhance the quality of life and contribute to the world economy. However, by the conclusion of 2024, ti is projected that the global network of the Internet of Things (IoT) will encompass over 207billion interconnected devices. The immense data collected and exchanged are often stored in centralized servers, leading to vulnerabilities such as data breaches. Blockchain technology, originally developed for cryptocurrency transactions, provides a possible solution for these challenges with its decentralized, immutable, and encrypted features. This survey provides a comprehensive discussion regarding the fusion of Blockchain technology and the loT, covering architectural alignment, application, security, and limitations. The contributions of this survey include an overview of Blockchain technology and its secure applications, the integration of Blockchain into IoT , an examination of suitable methods for Blockchain integration,and a taxonomy or security and limitations of loT and blockchain integration.

Background: Historically, in the United States and globally, women have supported other women before, during, and after childbirth. Birth doulas are considered an important component to eliminating racial disparities in maternal morbidity and mortality. To address disparities in birth outcomes, Healthy Women Healthy Futures (HWHF) provides free birth and postpartum doula services to low-income women and communities of color in New York City. Methods: Secondary analysis of program data (n=364) collected between 2020 and 2021 was conducted. Univariate and multivariable analyses focused on: method of delivery, preterm birth, low-birthweight birth, breastfeeding, maternal-infant skin contact. The key exposure variable was total doula hours. Results: Clients who received prenatal doula support had low rates of preterm birth (8.8%) and low birthweight (7.1%), and high rates of vaginal delivery (69.1%), breastfeeding in the hospital (90.9%), and maternal-infant skin contact (78.2%). Multivariable analysis did not identify an association between number of doula hours and any of the five outcomes examined. Conclusion: While descriptive results point to positive birth outcomes, these effects did not persist in multivariable analyses. Data quality concerns from this intervention likely hindered the ability to detect a program impact, emphasizing the importance of funding to support data collection and management activities.

The spread of fake news poses a serious threat to public trust and informed decision-making. The emergence of AI-generated fake news exacerbates this issue, as it can be produced more rapidly and convincingly than human-generated content. Addressing this issue, our paper introduces a robust approach to detect AI-generated fake news that is applicable across major disinformation domains: Politics and Elections, Health and Medicine, Science and Technology, and Entertainment and Celebrities. Our models differentiate between AI-generated and human-generated news articles while assessing their truthfulness. Utilizing large language models such as ChatGPT, we curated datasets spanning both AI and human generated fake and genuine news. Following extensive data preprocessing and the application of diverse feature extraction techniques, our models were trained using multiple machine learning algorithms. Our results show the feasibility of accurately identifying AI-generated fake news, thereby adding a significant layer of defense in combating misinformation across various sectors. In addition, we have explored the potential utility of transferable machine learning models trained on data from one domain (e.g., politics), when tested on another domain (e.g., health).

New York State Department of Transportation designates potentially hazardous conditions on bridges as flags. From 1982 until 2006 the flags issued for the bridges owned by New York City underwent all phases typical of crises, including a gradual increase, an exponential expansion, an extended peak, a gradual decline, and a convergence to a higher but manageable level. The attempts to forecast the flag pattern as it was developing are reviewed for possible relevance to management of the transportation infrastructure and in general.

Introduction: Wound healing is a complex cascade involving the orchestration of inflammatory cells, skin fibroblasts, keratinocytes, and endothelial cells. These cells achieve tissue repair through an interchange of cytokines and growth factors through a controlled inflammation, angiogenesis, and remodeling process. Disruptions in the wound-healing process by comorbid conditions lead to significant morbidity and mortality. Stem cell therapy has emerged as a new strategy to facilitate the wound healing process. Stem cells harvested from different sources can be used for wound repair and regeneration (endothelial progenitor cells, bone marrow, adipose tissue, dermal and inducible pluripotent stem cells). We aimed to review clinical, translational, and primary literature on stem cell therapies in chronic wounds and summarize practical clinical applications to dermatological care. Materials and Methods: A comprehensive search of Pubmed, Embase, Web of Science, Google Scholar, and Cochrane Library was conducted for studies relating to stem cells and wound healing. Duplicate results were removed using Covidence. Titles and abstracts were screened by two independent researchers (BF and KR), with discrepancies resolved through discussion and mutual agreement. Articles were excluded if they were review-type or commentary-type articles. Results: A total of 22,454 articles were retrieved from the search. Deduplication removed 905, and automation removed an additional 18,363 articles. After an initial screening of titles and abstracts, 170 articles remained. Full-text screening resulted in an additional 126 articles being removed, leaving 44 included studies. Discussion: Stem cells used for wound repair and regeneration include endothelial progenitor stem cells and adult stem cells, in the forms of bone marrow-derived mesenchymal stem cells, adipose tissue stem cells, and inducible pluripotent stem cells. Among these sources, adipose tissue-derived mesenchymal stem cells (AD-MSCs) are ideally used due to the abundant supply of fat tissue, ease of isolation, extensive proliferative capacities ex vivo, and their ability to secrete pro-angiogenic factors. AD-MSCs have been used to enhance wound healing in peripheral arterial disease, diabetic wounds, hypertensive ulcers, bullous diabeticorum, venous ulcers, and postsurgical wounds after Mohs micrographic surgery. These cells have been delivered to the tissue topically, with scaffolds, combined with plasma-rich proteins, and through atelocollagen in various human studies. These approaches, when combined with local wound care practices, resulted in decreased pain, shorter wound healing times, and better cosmesis. Conclusions: Stem cell transplantation is a potential therapeutic approach in the wound healing process. Transplanted stem cells do not only differentiate into multiple skin cell types but also provide cytokine and growth factors required for wound healing resulting in increased angiogenesis. Thus, this approach may be regarded as an attractive option for intractable wounds that cause major clinical problems, especially chronic lower leg wounds. It can also be used after Mohs micrographic surgery for defects left to heal by secondary intention. Stem cell application may reduce the overall cost burden on the healthcare system and improve the quality of life for patients.

We studied the dynamics of stable carbon (δ13C) and nitrogen (δ15N) isotopes in litters from Norway spruce (NSL) (Picea abies) and Scots pine (SPL) (Pinus silvestris) during in situ decomposition over a period of more than 4 years. Relative to initial values, δ13CNSL showed a weak enrichment (0.33‰), whereas δ13CSPL resulted in depletion (-0.74‰) at the end of decomposition. Both litter types experienced a depletion in δ15N during decomposition; δ15NNSL decreased by -1.74‰ and δ15NSPL decreased by -1.99‰. The effect of the selective preservation of or acid unhydrolyzable residue (AUR) in lowering δ13C of the residual litter was evident only in SPL. In the NSL, only in the initial stage C/N had a large effect on the δ13C values. In the later stages, there was a non-linear decrease in δ13CNSL with a simultaneous increase in AUR concentrations , but the effect size was large, suggesting the role of lignin in driving δ13C of residues in later stages. Depletion in 15N of the residual litters concomitant with the increase in N concentration suggests bacterial transformation of the litter over fungal components. A consistent decline in δ15N values further implies that bacterial dominance prompted this by immobilizing nitrate depleted in 15N in the residual litter.

In the century and a half since Maxwell first conjured his “finite being” which Lord Kelvin subsequently dubbed a “daemon”, researchers have explored the connections between non-equilibrium thermodynamics, entropy, and information theory. In recent years various Thermodynamic Uncertainty Relations (TURs) have been derived to inform upon the relationship between the entropy production and the precision possible in thermodynamic machines and processes. In this paper the recently derived TURs are applied to the hypothetical thermodynamic economy as described by Parker [1]. The TURs define the lower bound on the total entropy production of the economy. Changes in this entropy production rate has consequences on the stability of the economic system and plays a central role in the business cycle. This new perspective has important implications for policy makers, researchers, and other economic actors.

Sterol homeostasis in mammalian cells and tissues involves balancing three fundamental processes: de novo sterol biosynthesis; sterol import (e.g., from blood-borne lipoproteins); and sterol export. In complex tissues, composed of multiple different cell types (such as the retina), import and export also may involve intra-tissue, inter-cellular sterol exchange. Disruption of any of these processes can result in pathologies that impact the normal structure and function of the retina. Here, we provide a brief overview of what is known currently about sterol homeostasis in the vertebrate retina and offer a proposed path for future experimental work to further our understanding of these processes, with relevance to the development of novel therapeutic interventions for human diseases involving defective sterol homeostasis.

This study examines the exchange rate rebound effects of the Central Bank intervention in the selected ECOWAS economies. An empirical understanding of these effects is very important to trade adjustment as well as the macroeconomic stability in these countries. Using the panel data modelling framework, the study finds that the impact of the Central Bank intervention on exchange rate is insignificant and it does not lead to the exchange rate rebound. In addition, money supply as well as monetary policy rate implemented by the monetary authorities significantly influences the level of exchange rate in a positive direction.

Stock price prediction has long been a critical area of research in financial modeling. The inherent complexity of financial markets, characterized by both short-term fluctuations and long-term trends, poses significant challenges in accurately capturing underlying patterns. While Long Short-Term Memory (LSTM) networks have shown strong performance in short-term stock price prediction, they struggle with effectively modeling long-term dependencies. In this paper, we propose an advanced stock price prediction model based on the Extended Long Short-Term Memory (xLSTM) algorithm, designed to enhance predictive accuracy over both short and long-term periods. We conduct extensive experiments by building and evaluating models based on xLSTM and LSTM architectures for multiple stocks. Our results demonstrate that the xLSTM model consistently outperforms the LSTM model across all stocks and time horizons, with the performance gap widening as the prediction period extends. The observations underscore the superior capability of the xLSTM-based model to capture long-term patterns in financial data, offering a promising approach for more accurate and reliable stock price predictions.

This study presents the Distilled MixUp Squeeze ResNet (DMSResNet), an optimized variant of the Residual Network architecture designed for high accuracy and computational efficiency. By integrating Squeeze-and-Excitation blocks, MixUp data augmentation, and knowledge distillation, it achieves competitive performance within strict parameter constraints and limited training resources. Evaluated on the CIFAR-10 dataset, the model attains a 96.56% accuracy with only 4.3 million total parameters. The results demonstrate that optimization of established architectures can yield performance comparable to newer models, especially in resource-limited scenarios. This work contributes to the development of efficient deep learning models for applications in constrained computational environments.

This study explores the significance of active suspension systems for vehicles with lower chassis compared to conventional ones, aiming at the development of future automobiles. Conventional linear MR (Magneto-Rheological) dampers were found inadequate in ensuring sufficient vibration control because the vehicle's chassis becomes lowered in the unmanned vehicles or purposed based vehicles. As an alternative, a rotary type of MR damper is proposed in this work. The proposed damper is designed based on pre-specified design parameters through mathematical modeling and magnetic field analyses. Subsequently, a prototype of the rotary MR damper identical to the design is fabricated, and effectiveness is shown through experimental investigations. In configuring the experiments, a proportional-integral (PI) controller is employed for current control to reduce the response time of the damper. The results presented in this work provides useful guidelines to develop a new type of MR damper applicable to various types of future vehicles’ suspension systems with low distance from the tire to the body floor.

Diffusion magnetic resonance imaging (dMRI) is an imaging technique that provides information on the microstructural organization of biological tissue. dMRI allows for non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or higher, can boost signal-to-noise ratio (SNR) to improve dMRI compared with what is attainable at conventional field strengths such as 3T or 1.5T. However, at 7T wavelength effects cause lower transmit magnetic field efficiency in the human brain, mainly in the posterior fossa region, manifesting as signal dropouts in this region. Recently, we reported a simple approach of using a wireless radiofrequency (RF) surface array to improve transmit efficiency and signal sensitivity at 7T. Here we demonstrate the feasibility and effectiveness of using this RF enhancer for in-vivo dMRI at 7T. The use of the RF enhancer can effectively recover signal dropouts in the regions with intrinsically lower SNR such as cerebellum, leading to a better depiction of principal fiber orientations and enhanced visualization of extended tracts to the brainstem and pons enabling more comprehensive delineation of the full white matter architecture.

Background: Depression presents with altered energy regulation and neural plasticity. Previous electroencephalography (EEG) studies showed that practice increases power in beta range (13-30 Hz) in healthy subjects but not in those with impaired plasticity. Here, we ascertain whether depression presents with alterations of spectral activity and connectivity before and after practice. Methods: We used publicly available resting state EEG (64 electrodes) of 122 subjects. Based on Beck Depression Inventory (BDI) score, they were assigned to either high BDI (hBDI, BDI>13, N=46) or control (CTL, BDI<7, N=75) group. We analyzed spectral activity, theta-beta and theta-gamma phase-amplitude coupling (PAC) of EEG recorded before and after a learning task. Results: At baseline, compared to CTL, hBDI group exhibited greater beta power over fronto-parietal regions and gamma over right parieto-occipital area. After practice, increases in all frequency ranges were observed only in CTL. Theta-beta and theta-gamma PAC were greater in hBDI before the task but not after the task. Conclusions: The lack of substantial post-task growth of beta power in depressed subjects likely represents power saturation due to greater baseline values. We speculate that inhibitory/excitatory imbalance, altered plasticity mechanisms, and energy dysregulation present in depression may contribute to this phenomenon.

Effective leadership is reflected by evidence-based attributes, competencies, and behaviors. The Future of Nursing Report (2011) identified a gap in leadership knowledge, recommending development of creative and innovative strategies to strengthen nursing leadership curricula. These results provided an impetus and charge for nursing education to reexamine and revise leadership content. This mixed methods study analyzed baccalaureate and masters nursing students’ development of their leadership knowledge and skills. Through reflections, undergraduate (n=60) and master’s (n=52) students identified their perceived priorities and tips for success for nursing leaders across the healthcare continuum. Qualitative content analysis aligned students’ comments with the American Organization for Nursing Leadership (AONL) Nurse Executive Competencies (2015). Descriptive data provided the summaries of which leadership competencies were represented. Alignment to the competencies varied, commensurate with the level of educational program: BSN-level management skills vs. MS-level executive skills. Common responses across both programs prioritized communication, professionalism, and leadership behaviors. The results provide an opportunity for students’ synthesis of leadership skills and practices, may guide educators in the design of multi-level leadership curricula, and support development of clinical leaders in practice. This study was prospectively registered with the Campus Institutional Review Board on January 23, 2023, protocol code: 0086-001.

We have designed cell penetrating peptides that target the leucine zipper transcription factors ATF5, CE-BPB and CEBPD and that promote apoptotic death of a wide range of cancer cell types, but not normal cells, in vitro and in vivo. While such peptides have the potential for clinical application, their mechanisms of action are not fully understood. Here, we show that one such peptide, Dpep, compromises glucose uptake and glycolysis in a cell-context-dependent manner (in about 2/3 of cancer lines assessed). These actions are dependent on induction of tumor suppressor TXNIP (thioredoxin interacting protein) mRNA and protein. Knockdown studies show that TXNIP significantly contributes to apoptotic death in those cancer cells in which it is induced by Dpep. The metabolic actions of Dpep on glycolysis led us to explore combinations of Dpep with clinically approved drugs metformin and atovaquone that inhibit oxidative phosphorylation and that are in trials for cancer treatment. Dpep showed additive to synergistic activities in all lines tested. In summary, we find that Dpep induces TXNIP in a cell-context-dependent manner that in turn sup-presses glucose uptake and glycolysis and contributes to apoptotic death of a range of cancer cells.

This is a single center retrospective study to assess safety and tolerability of continuous inhaled iloprost use as rescue therapy for refractory pulmonary hypertension (PH) in critically ill neonates and infants. Retrospective chart review was performed on 58 infants and data was collected at baseline, 1, 6, 12, 24, 48, and 72 hours of iloprost initiation. Primary outcomes were change in heart rate (HR), fraction of inspired oxygen (FiO2), mean airway pressures (MAP), blood pressure (BP) and oxygenation index (OI). Secondary outcomes were need for extracorporeal membrane oxygenation (ECMO) and death. 51 patients treated for > 6 hours were analyzed in 2 age groups, neonate (≤28 days: n=32) and infant (29-365 days: n=19). FiO2 (p

This paper comprehensively explores the integration of cloud computing and advanced recommendation systems, emphasizing their pivotal roles in enhancing user experiences and operational efficiencies across digital platforms. It reviews the evolution of recommendation algorithms, highlighting their application in diverse domains such as e-commerce and media. The study evaluates the performance of advanced models like UniLLMRec against traditional counterparts using datasets from news and e-commerce domains. Additionally, the paper discusses the infrastructure architecture of cloud computing, demonstrating its capability to support scalable and efficient data processing. Through experimental insights and methodology, the research underscores the transformative impact of cloud technologies on optimizing recommendation system performance, thereby advancing digital engagement and competitiveness.

This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology Medication-led Health care (RLHC) to facilitate the development and application of personalised treatment protocols, and to improve diagnostic accuracy, prognosis assessment, and treatment recommendations by uploading quantitative image features to a shared database and comparing them with historical images.

Prochlorococcus, a cyanobacteria genus of the smallest and most abundant oceanic phototrophs, encompasses ecotype strains adapted to high-light (HL) and low-light (LL) niches. To elucidate the adaptive evolution of this genus, we analyzed 40 Prochlorococcus marinus ORFeomes, including two cornerstone strains, MED4 and NATL1A. Employing deep learning with robust statistical methods, we detected new protein family distributions in the strains and identified key genes differentiating HL and LL strains. HL strains harbor genes (ABC-2 transporters) related to stress resistance, such as DNA repair and RNA processing, while LL strains exhibit unique chlorophyll adaptations (ion transport proteins, HEAT repeats). We report variable, depth-dependent endogenous viral elements in the 40 strains. We constructed the ORFeomes of MED4 and NATL1A covering 99 % of the annotated protein-coding sequences of the two species, totaling 3976 cloned, sequence-verified ORFs. These comparative genomics analyses, paired with MED4 and NATL1A ORFeomes, will facilitate future genotype-to-phenotype mappings and systems biology exploration of Prochlorococcus ecology.

The goal of the investigation is to increase coverage, which is ensure better transmission around IoT services using IRS within a two-tier 6G system comprising of one micro cell tier running beneath a macro cellular tier. As a result, the study tested and compared the reliability of traditional connectivity with IRS-enhanced connectivity in terms of upstream end-to-end latency using computer-based measuring methods. The study concluded that the implementation of IRS greatly improves coverage, which means transmission effectiveness, for 6G services.

Immune responses to influenza (flu) antigens reflect memory to prior infections or vaccinations, which might influence immunity to new flu antigens. Memory of past antigens has been termed “original antigenic sin” or more recently “immune imprinting” and “seniority”. We have researched a comparison between the immune response to live flu infections and inactivated flu vaccina-tions. A brief history of antibody generation theories is presented culminating in new findings about the immune-network theory suggesting that a network of clones exists between an-ti-idiotypic antibodies and T cell receptors. Findings regarding the 2009 pandemic flu strain and immune responses to it are presented, including memory B cells and conserved regions within the hemagglutinin protein. The importance of CD4+ memory T cells and cytotoxic CD8+ T cells responding to both infections and vaccinations are discussed and compared. Innate immune cells like natural killer (NK) cells and macrophages are discussed regarding their roles in adaptive immune responses. Antigen presentation via macroautophagy processes are described. New vaccines in development are mentioned along with the results of some clinical trials. The manu-script concludes with how repeated vaccinations are impacting the immune system and a sketch of what might be behind the imprinting phenomenon, including future research directions.

This research presents an ensemble model developed to enhance the safety of bedridden patients in healthcare settings. The model utilizes a unique dataset capturing six typical movements performed by patients laying in bed, such as rolling over, falling off the bed, etc. The aim is to automate the classification of these movements, thereby enabling real-time monitoring of patients without the constant presence of hospital personnel in the room, a common practice in current healthcare settings. The dataset is preprocessed and balanced using the Synthetic Minority Over-sampling Technique, and then split into training and test sets for model training and evaluation. Three different models are explored: a Decision Tree Regressor, a Gradient Boosting Regressor, and a Bagging Regressor. The Bagging Regressor is an ensemble model that utilizes the Decision Tree Regressor as its base regressor. Each model’s performance is evaluated using accuracy, R2 score, and Mean Squared Error on both the training and test sets. The ensemble model demonstrates promising results, indicating the potential of machine learning to improve patient safety in healthcare settings. The study also employs K-Fold cross-validation and learning curves to validate the robustness of the ensemble model. This research contributes to the growing body of knowledge on the application of machine learning in healthcare and opens up new avenues for improving patient safety through real-time monitoring and classification of patient movements.

This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli, an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluid (MRF), magnetorheological elastomer (MRE) and magnetorheological plastomer (MRP). To easily understand the history of sensing research using the two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss on the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensor, tactile device, strain sensor, wearable bending sensor, magnetometer, display device and flux measurement sensor. After briefly describing what have been reviewed in a conclusion, several challenging future works, which should be done for practical applications as sensors or sensing devices, are remarked in terms of new technologies such as artificial intelligence neural network in which several parameters affecting the sensor signals can be precisely tuned or controlled. It is sure that this review article is very helpful to make creative sensors using not only the proposed smart materials but also several different types of smart materials including shape memory alloys and active polymers.

Protein Kinase C delta (PKCδ) has emerged as a key protective molecule against systemic lupus erythematosus (SLE or lupus), an autoimmune disease characterized by anti-double stranded (ds) DNA IgGs. Although PKCδ deficient mice and lupus patients with mutated PRKCD genes clearly demonstrate the requirement of PKCδ for preventing lupus autoimmunity, this critical tolerance mechanism remains poorly understood. We recently reported that PKCδ selectively deletes anti-dsDNA B cells in the germinal center (GC), disclosing a key B cell tolerance mechanism that prevents lupus autoimmunity. PKCδ’s tolerance function is activated by sphingomyelin synthase 2 (SMS2), a lipid enzyme whose expression is generally reduced in B cells from lupus patients. Moreover, pharmacologic strengthening of the SMS2/PKCδ tolerance pathway alleviated lupus pathogenesis in mice. Here, we review relevant publications in order to provide mechanistic insights into PKCδ’s tolerance activity, and discuss the potential significance of therapeutically targeting PKCδ’s tolerance activity in the GC for selectively inhibiting lupus autoimmunity.

Pregnancy-related morbidity and mortality remains high across the United States and the majority of deaths are preventable. Misdiagnosis and delay in diagnosis are thought to be contributors to preventable harm. These diagnostic errors in obstetrics are understudied. We present selected research methods to ascertain rates of and harm associated with diagnostic errors, the challenges in investigating them, and present future steps toward achieving diagnostic excellence in obstetrics.

Pattern recognition has long been regarded as key role for crime prevention and reduction. Crime analysts and policy makers can formulate effective strategies and allocate resources with reference to spatial and temporal pattern of crime. In order the combat and prevent severe crime in New York City (NYC), this study analyzed Felony Crime data of NYC in previous 5 years (2015 2020) and discovered criminal hotspots pattern and temporal patterns with open criminal complaint data provided by New York Police Department (NYPD). This study adapt a human computer interactive appraoch to draw patterns from crime data, whereas computations and visualization are performed by Python libraries, and human to inform the decision of visualization methods, computational parameters and direction of this exploratary analysis. Density based clustering algorithms, Grid Thematic Mapping and Density Heatmap are displayed to identify hotspots and demonstrates their associations with spatial features. Timeline analysis on moments of crime occurance demonstrates seasonality where crimes are mostly commited, while aoristic analysis showed hours of day when crime is mostly committed considering their timespan. Lastly, 3D visualization improved recognition of the displacement of hotspot over time, and suggested long term hotspots in NYC in 3 D visualization. This inform strategic plans for police deployment.

The dynamic nature of international business necessitates a strategic approach to achieve sustainable value creation. This paper supports the hypothesis that understanding institutional constraints and leveraging organizational resources provides the foundation for sustainable value creation during international expansion. These elements provide a solid foundation for firms seeking to expand internationally by enabling them to effectively navigate the complexities of foreign markets. Using the context supplied by Peng (2023), the subsequent sections and subsections delve into critical facets of international expansion, including market integration, the role of entrepreneurial firms, entry strategies, supply chain establishment, multinational strategies, and the impact of global trade and foreign direct investment (FDI).

Ambiguity exists over treatment and surveillance strategies after endoscopic submucosal dissec-tion (ESD) for esophageal squamous cell neoplasia (ESCN) with unfavorable histologic features. This study investigated the long-term outcomes of ESD in high-risk ESCN patients. We retro-spectively included early ESCN patients treated with ESD at two medical centers in Taiwan be-tween August 2010 and December 2023. Demographic, endoscopic, and pathological data were collected. Among 146 patients (mean age 59.17 years) with 183 lesions, 73 (50%) had a history of head and neck cancer (HNC). En bloc and R0 resection were achieved in 100% and 95.6% of the lesions, respectively. The 5-year overall survival (OS), disease-specific survival (DSS) and local recurrence rates were 42.7%, 94.7% and 11%. R0 resection was significantly associated with re-currence in univariate analysis (adjusted OR: 0.23, 95% CI: 0.05-0.99, P=0.05). Alcohol abstinence was independently associated with lower recurrence (adjusted OR: 0.20, 95% CI: 0.08-0.48, P

Market crises pose significant challenges for businesses, emphasizing the importance of effective crisis management strategies. Central to these strategies is the ability to identify and retain loyal customers, who often serve as the bedrock of stability during tumultuous times. This paper investigates the application of deep meta-learning analysis to predict loyal customers as a cornerstone of market crisis management. Drawing upon an extensive literature review, the study explores previous research on market crises, customer loyalty, and the evolution of deep learning and meta-learning in crisis management contexts. Methodologically, the paper outlines data collection, model development, and evaluation procedures tailored for deep meta-learning-based customer prediction. In results, the overall accuracy of model 85%, a precision 0.88, recall 0.82, and F1-score 0.85 were obtained. The analysis demonstrates the effectiveness of deep meta-learning models in accurately identifying loyal customers during market crises, offering insights into their performance and applicability compared to traditional methods. Practical implications include potential applications in crisis management for businesses and considerations for real-world implementation.

Obesity has been identified as an emerging health concern for Chinese American children; however, very little is known about diets in Asian American children. The objective of our paper was to describe the dietary intakes of urban Chinese American schoolchildren using a state-of-the-art approach for dietary assessment. Data for this analysis come from the Food Journal Project 2017, a pilot and feasibility study conducted by a multi-sector collaboration. Children aged 8-12 (n=83) completed two dietary assessments using a food diary from January-June 2017. Children were then interviewed using the food diary as a guide and dietary data were entered into the online ASA24 system by study staff. Chinese American children were identified using surname, and were compared to non-Chinese peers with respect to nutrient intake and the Healthy Eating Index 2010 (HEI-2010). Chinese American children consumed more sodium dense diets, more protein, and less sugar compared to non-Chinese children. With regards to the HEI-2010, Chinese American children had less favorable whole grains and sodium scores; and more favorable seafood protein and empty calories scores compared to non-Chinese children. Sodium reduction and increasing whole grain intakes may be warranted in this group, but should be verified with additional studies.

Point defects induced by doping rare earth elements RE (Nd, Er) into the magnesium oxide host were investigated by classical atomistic simulations utilising the General Utility Lattice Program (GULP). Formation and association energies were calculated for the potential defect structures. Both isolated defects and defect complexes were considered. The most energetically favourable structures of defect complexes were found for rare earth doped and Li co-doped systems. The correlation between the association energy and the structure of the defect complex was investigated. The influences of Li were revealed with respect to energy and structure. The simulation results contribute to the understanding of the point defects of doped MgO and how Li influences the doping of rare earth elements in the MgO host.

The study employed simulation-based measurement approaches to evaluate and compare the performance of conventional networks to IRS-enhanced systems in terms of upstream spectral efficiency. The study discovered that applying IRS greatly improves coverage, or transmitting potency, for 6G services.

The draconian regulation required by the inherent instability of cognitive phenomena -- gene expression, immune function, cancer suppression, wound healing, animal consciousness, machine intelligence, network stabilization, institutional cognition, and their many and varied composites -- can be viewed through the lens of the asymptotic limit theorems of both information and control theories. Here, we explore the dynamics and sometimes highly punctuated failures of the regulation of cognition under increasing `noise'. The approach parallels, and indeed generalizes, the Data Rate Theorem of control theory, extending the theorem's requirement of a minimum channel capacity necessary for stabilization of an inherently unstable system. Various models are explored across different basic underlying probability distributions characteristic of the system under study, and across different hierarchical scales, finding the addition of adaptive -- learned -- regulation greatly extends the reach of innate -- e.g., AI-driven or otherwise pre-programmed -- regulation. This work points toward the construction of new statistical tools for the analysis of observational and empirical data across a wide spectrum of inherent pathologies and adversarial challenges afflicting a wide variety of cognitive processes.

A typhoon-induced storm surge is considered one of the most severe coastal disasters in Taiwan. However, the combination of the storm surge and the astronomical tide called the storm tide can actually cause extreme flooding in coastal areas. This study implemented a two-dimensional hydrodynamic model to account for the interaction between tides and storm surges on the coast of Taiwan. The model was validated with observed water levels at Sauo Fish Port, Hualien Port, and Chenggong Fish Port under different historical typhoon events. The model results are in reasonable agreement with the measured data. The validated model was then used to evaluate the effects of the typhoon's intensity, bathymetric change, and the combination of the typhoon’s intensity and bathymetric change on the maximum storm tide and its distribution along the east coast of Taiwan. The results indicated that the maximum storm tide rises to 1.92 m under a typhoon with an intensity of a 100-year return period. The maximum storm tide increased from a baseline of 1.26 m to 2.63 m for a 90% bathymetric rise at Sauo Fish Port under the conditions of Typhoon Jangmi (2008). The combination of the intensity of a typhoon with a 100-year return period and a 90% bathymetric rise will result in a maximum storm tide exceeding 4 m, 2 m, and 3 m at Sauo Fish Port, Hualien Port, and Chenggong Fish Port, respectively. We also found that the distribution of the maximum storm tide on the east coast of Taiwan can expand significantly subject to the bathymetric rise.

There are some limitations in using the Body Condition Score (BCS) for client education to prevent obesity, hyperlipidemia-related diseases, and orthopedic diseases in dogs because it is hard to quantify in detail. In small dogs, patellar luxation is a common orthopedic disease that is related to obesity and hind leg muscle. In this study, we evaluated the thorax/thigh circumference ratio as a prognostic evaluation index, along with the BCS, for assessing patellar dislocation and other hyperlipidemia-related diseases. I analyzed eleven client-owned dogs that visited Bundang New York Animal Hospital, South Korea. According to the results, triglycerides (TGs) showed a negative correlation with thorax/thigh value (R = -0.585, p-value = 0.059) and a strong positive correlation with thigh circumference (R = 0.749, p-value = 0.008). Total cholesterol (TC) showed a strong positive correlation with thigh circumference (R = 0.776, p-value = 0.005), whereas the thorax/thigh value showed a negative correlation with the medial patella luxation (MPL) grade with low significance (R = -0.343, p-value = 0.302). These data indicate that thigh circumference can be an excellent negative indicator for hyperlipidemia and thorax/thigh value shows a low correlation with medial patella luxation, which encompasses other factors including varus and trochlear groove, despite the limitations of this study due to the small sample size.

This paper brings to fruition recently found \cite{migdal2023exact} exact reduction of decaying turbulence in the Navier-Stokes equation in $3 + 1$ dimensions to a Number Theory problem of finding the statistical limit of the Euler ensemble. We reformulate the Euler ensemble as a Markov chain and show the equivalence of this formulation to the \textbf{quantum} statistical theory of $N$ fermions on a ring, with an external field related to the random fractions of $\pi$. We find the solution of this system in the turbulent limit $N\to \infty, \nu \to 0$ in terms of a complex trajectory (instanton) providing a saddle point to the path integral over the density of these fermions. This results in an analytic formula for the observable correlation function of vorticity in wavevector space. This is a full solution of decaying turbulence from the first principle without approximations or fitted parameters. The energy spectrum decays as $k^{-\frac{7}{2}}$. We compute resulting integrals in \Mathematica{} and present effective index $n(t) = -t\partial_t \log E $ for the energy decay as a function of time Fig.\ref{fig::NPlot}. The asymptotic value of the effective index in energy decay $n(\infty) = \frac{5}{4}$, but the universal function $n(t)$ is neither constant nor linear.

Lung macrophage cells play a critical role in various lung disease, and their state can change depending on the progression of the disease by inducing either an inflammatory or anti-inflammatory state. In this review, we investigate the potential therapeutic effects of treatment with antioxidant nanoparticles in airway disease focusing on porcine reproductive and respiratory virus (PRRSV), considering Reactive Oxygen Species (ROS) as one of the factors that regulate M1 and M2 macrophages in the inflammatory and anti-inflammatory states, respectively. In addition, we examine the status of protein structure research on CD163 (one of the markers of anti-inflammatory M2 macrophages) in human and veterinary lung diseases and suggest directions for future applicable research areas.

One of the efficient data mining tools is density-based clustering, including the density grid-based clustering. However, a common drawback always existing in clusters made by the density grid-based method is the existence of weakly connected grids deriving mainly from noise. Appearing such an unwanted connection with a high frequency reduces the accuracy of the obtained cluster data space (CDS) and its application efficiency. Here, we present an essential improvement to overcome this problem. First, we describe a concept of the weak-connected grid cell (WCG) and present a fuzzy-type approximation to depict the density-based distribution of data points at grid nodes. Then, we propose a strategy of searching WCG for density grid-based clustering (SWCG-DGB) to set up a CDS, filter noise, and tune the created CDS. A buffer is deployed during this phase to collect border points and filter noise, which improves the computational time significantly, especially for noisy datasets. Results from numerical surveys reflected the compared efficiency of this method in clustering validity, including the accuracy of the number of clusters.

Traditional classroom teaching model can no longer meet the needs of students' ability development. As a new model actively advocated in the modern education concept, personalized teaching focuses on the development needs of students and emphasizes respect for students' individualized growth. In this work, we utilize deep learning method to recommend teaching strategies for different features of students. Specifically, the proposed method includes the construction of a multi-layer neural network model, which can analyze students' multi-dimensional learning data and behavior patterns, so as to recommend the most suitable teaching strategies for individual students. Through data collection and preprocessing, model architecture design, model training and optimization, the model gradually improves the accuracy of prediction and recommendation. Experimental analysis verifies the effectiveness of the model through field teaching experiments, and the results show that the personalized teaching strategy based on deep learning can significantly improve the learning effect and participation of students.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

Urban forests are critical infrastructure for mitigating environmental and social challenges cities face. Municipalities and non-governmental entities, among others, often set goals (e.g., tree planting or canopy targets) to support urban forests and their benefits. We focus on canopy goals and develop conceptual underpinnings for an analysis of where additional canopy, as one important dimension of the urban forest, can fit within the landscape, while considering factors that influence where trees can be planted and where canopy can grow – ‘practical canopy.’ We apply this in New York City (NYC) to inform the setting of a canopy goal by the NYC Urban Forest Task Force (UFTF) for the NYC Urban Forest Agenda, which may trigger a virtuous cycle that supports the urban forest there. We further develop framing for a ‘priority canopy’ analysis to understand where urban forest expansion should be prioritized given more context (e.g., environmental hazards, local preferences), which can inform how expansion of the urban forest is achieved. We estimate an opportunity for 15,899 ha of new canopy in NYC given existing opportunities and constraints (practical canopy), which, if leveraged, could result in nearly doubling the canopy as of 2017 (17,253 ha). However, like existing canopy, practical canopy is not evenly distributed, in general, or across jurisdictions and land uses. Relying solely on areas identified as practical canopy to expand the urban forest would exacerbate inequities in its distribution. We discuss how the NYC UFTF established an aspirational but achievable goal of 30% canopy cover by 2035, which was informed by this analysis and guided by priorities of equity, health, and resilience. Achievement of this goal will ultimately require a combination of protecting and stewarding the existing resource, and leveraging opportunities for tree planting. Achieving a more equitable urban forest will also require identification of priority canopy, and, in cases, creation of new opportunities for tree planting and canopy expansion. Overall, the collaborative establishment of such goals based on local context can be instrumental in creating a virtuous cycle, moving conservation actors toward exercising influence and agency within the social ecological system.

Chronic eosinophilic pneumonia (CEP) is an idiopathic disorder characterized by an abnormal and marked accumulation of eosinophils in the interstitial and alveolar spaces of the lung. Idiopathic chronic eosinophilic pneumonia is reported to comprise anywhere from 0-2.5% of cases within the registries of interstitial lung disease. Diagnosis is based on the clinical constellation of symptoms, characteristic radiographic findings and peripheral blood or BAL eosinophilia, in the absence of infection or drug-induced eosinophilia. There is no consensus on the dose and duration of treatment, but most authors recommend initial doses of prednisone at 0.5–1 mg/kg/day with gradual tapering of the dose for total treatment duration of 6–12 months.

We report a facile route for the green synthesis of trimethylchitosan nitrate-capped silver nanoparticles (TMCN-AgNPs) with positive surface charge. In this synthesis, silver nitrate, glucose, and trimethyl chitosan nitrate (TMCN) were used as silver precursor, reducing agent, and stabilizer, respectively. The reaction was carried out in a stirred basic aqueous medium at room temperature without the use of energy-consuming or expensive equipment. We investigated the effects of the concentrations of NaOH, glucose, and TMCN on the particle size, zeta potential, and formation yield. The AgNPs were characterized by UV-visible spectroscopy, photon correlation spectroscopy, laser Doppler anemometry, transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The catalytic activity of the TMCN-AgNPs was studied by the reduction of 4-nitrophenol using NaBH₄ as a reducing agent. We evaluated the antibacterial effects of the TMCN-AgNPs on Acinetobacter baumannii, Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus using the broth microdilution method. The results showed that both gram-positive and gram-negative bacteria were killed by the TMCN-AgNPs at very low concentration (< 6.13 μg/mL). Moreover, the TMCN-AgNPs also showed high antibacterial activity against clinically isolated multidrug-resistant A. baumannii strains, and the minimum inhibitory concentration (MIC) was ≤ 12.25 μg/mL.

The objective of this work is to analyze the improvement of heat transfer in trapezoidal fin heat exchangers using numerical simulation. Heat exchangers are essential components frequently used in contemporary electronic gadgets to effectively disperse heat produced by high-power components. Nevertheless, as electronic devices continue to advance and become more powerful, the heat transfer capacity of heat exchangers is facing more requirements, therefore requiring enhanced heat dissipation efficiency. Afterwards, various fin angles were adjusted, and the heat transmission efficiency was compared. Modifying the angles can significantly improve the effi-ciency of convective heat dissipation. Moreover, it has been found that employing an optimized trapezoidal fin structure can further enhance the efficiency of heat transfer. The results indicate that trapezoidal fin heat exchangers provide superior heat dissipation efficiency compared to conventional heat exchangers. This is important for enhancing the thermal dissipation capabili-ties of electrical gadgets, hence guaranteeing steady functionality and extending their longevity. Subsequent study might explore supplementary methods to boost heat transmission in different heat exchanger configurations in order to meet the growing requirements for heat dissipation.

In veterinary medicine, the significance of High-Density Lipoprotein (HDL) measurements is not well-documented than it is in humans. The HDL level can be measured in dogs, and through referring to the normal range, it is possible to find out what this means for various endocrine diseases and hyperlipidemia diseases. Aim of this study is to measure the HDL levels in dogs with various conditions and to evaluate whether the Total Cholesterol (TC), HDL ratio is effective as a prognostic indicator in various hyperlipidemia and endocrine diseases. Through prospective clinical trial, sixteen client-owned dogs and cats visiting a local private practice were divided into three groups; five dogs without hyperlipidemia or metabolic disease (Group 1); eight animals with Cushing's, hypothyroidism, and gallbladder sludge (Group 2) and three diabetics, urinary disorders, and healthy cats (Group 3). In two dogs, the TC/HDL values were between 2 and 3; in two dogs, the values were between 3 and 4; in two dogs, the values were between 4 and 5. In three dogs, the TC/HDL values were between 5 and 6 and in three dogs, the ratio values were between 6 and 7. The other value was higher than 8. Except two dogs which showed lower value than 3, all dogs had concurrent endocrine disease including Cushing’s disease, hypothyroidism, gall bladder mucocele, hyperlipidemia. There was a difference in each group in terms of the TC/HDL value compared to the group with underlying disease. This means that TC/HDL values can be an excellent indicator of endocrine disease in dogs as well. In cats, although this is a very small sample, a high TC/HDL value of 9 points are shown in the cat which had diabetes mellitus. However, for more statistically significant results, a larger sample group for further investigation is needed.

Quantum physics is intrinsically probabilistic. The entropy of a quantum state quantifies the amount of randomness (or information loss) of such state. The degrees of freedom of a quantum state are position and spin, and upon their specification Born's rule in phase space defines randomness. We focus on the spin degree of freedom and elucidate the spin entropy. Then, we present some of its properties and show how entanglement increases spin entropy. Some speculative predictions on the decay of the $Z^0,W^{+,-}$ gauge bosons conclude the paper.

It has been declared repeatedly that cancer is a result of molecular genetic abnormalities. However, there has been no working model describing the specific functional consequences of the deranged genomic processes that result in the initiation and propagation of the cancer process during carcinogenesis. We no longer need to question whether or not cancer arises as a result of a molecular genetic defect within the cancer cell. The legitimate questions are: how and why? This article reviews the preeminent data on cancer molecular genetics and subsequently proposes that the sentinel event in cancer initiation is the aberrant production of fused transcription activators with new molecular properties within normal tissue stem cells. This results in the production of vital oncogenes with dysfunctional gene activation transcription properties, which leads to dysfunctional gene regulation, the aberrant activation of transduction pathways, chromosomal breakage, activation of driver oncogenes, reactivation of stem cell transduction pathways and the activation of genes that result in the hallmarks of cancer. Furthermore, a novel holistic molecular genetic model of cancer initiation and progression is presented along with a new paradigm for the approach to personalized targeted cancer therapy, clinical monitoring and cancer diagnosis.

Despite ample warning and significant lead time, global society has not acted sufficiently to combat climate change, and global carbon dioxide emissions are now about 60% higher today than in 1990 [1]. This paper uses marine fisheries as an analog model to compare with the many similar management issues faced with global climate change. Both marine fisheries and global climate change are complex systems that have similar properties that make management very difficult. A simple fishery prototype model is used to educate citizens about similar issues of dynamic complexity found in climate change models. We highlight how our slow reaction time to global climate change can be thought of as a mismatch between our cognitive abilities and the nature of global collective problems. Understanding the history and system structure of marine fisheries can provide insight into managing global climate change. We suggest ways to improve communication between science and citizens for updating mental models and changing climate policy.

Urban areas have very complex spatial structures. These spatial structures are primarily composed of a complex network of built environments, which evolve rapidly as the cities expand to meet the growing population’s demand and economic development. Therefore, studying the impact of spatial structures on urban heat patterns is extremely important for sustainable urban planning and growth. We investigated the relationship between surface temperature obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, at 90 m spatial resolution) on the current EOS-Terra platform and different urban components based on the classification of high-resolution QuickBird imagery. We further investigated the relationships between surface temperature and building footprint and land use information acquired from the New York City (NYC) Department of City Planning. The ASTER image reveals fine-scale urban heat patterns in the NYC metropolitan region. The dark and medium-dark impervious surfaces, along with bright surfaces, generate higher surface temperatures. Even with highly reflective urban materials, the presence of impervious materials leads to an increased surface temperature. At the same time, trees and shadows are effective in reducing urban heat. The data aggregated to the census tract reveals high-temperature clusters in Queens, Brooklyn, and the Bronx region of NYC. These clusters are associated with industrial and manufacturing areas and multi-family walk-up buildings as dominant land use. The census tracts with more trees and higher building height variability generate lower surface temperatures, consistent with shadow cast by high-rise buildings and trees. The results of this study can be valuable for urban heat island modeling on the effects of building heights variability and tree shadows on small-scale surface temperature patterns. It can also help identify the risk areas during extreme heat events to protect public health.

The paper addresses the energy consumption minimization problem in wireless powered communication networks (WPCNs) and proposes a DT-SWIPT-assisted time allocation scheme, named DaTA, such that the wireless station can share the remaining energy after transmission to the HAP to those who have not transmitted to the HAP to minimize the energy consumption of the network. In addition to proposing a new frame structure, the paper also considers the SNR (Signal-to-Noise) constraint to guarantee that the HAP can successfully receive data from wireless stations. In the paper, the energy consumption minimization problem is formulated as a nonlinear programming model. We employ the SQP (Sequential Quadratic Programming) algorithm to figure out the optimal solution. Moreover, a heuristic method is proposed as well to obtain a near-optimal solution in a shorter time. Simulation results showed that the proposed scheme can have better performance than the related work in terms of energy consumption and energy efficiency.

The vast array of interconnected microbes across earth’s ecosystems and within holobionts has been called the “Internet of Microbes.” Research into the fundamental microbial properties has demonstrated that bacteria and archaea are masters of energy and information collection, storage, transformation, and dissemination using both “wired” and wireless (at a distance) capacities. These features suggest that microbes are quantum entangled within and between earth’s holobionts, and that specific tools affecting energy and information functions could provide an effective strategy for managing microbial populations. Microbes use four critically important microbial networking functions (sound, light, electricity, and magnetism). This narrative review focuses on microbial management based on two of the four: sound and light (as well as related vibrations). These are examined as: 1) tools for managing microbial populations, 2) tools to support new technologies, and 3) tools for healing humans and other holobionts. Given microbial sensitivity to sound, light, and vibrations, it is critical that microbial populations and microbe-laden holobionts are protected from potentially damaging exposures. We conclude that sound, light, and vibrations are useful therapeutic tools that can help support useful microbial populations and help to address ongoing challenges of holobiont disease.

Human papillomavirus (HPV), a DNA virus, causes one the most common sexually transmitted infections (STI), spreading through direct sexual contact to the anogenital and oropharyngeal regions. Although most infected individuals may be asymptomatic and naturally undergo viral clearance, in some individuals HPV causes carcinogenesis of the infected epithelia. HPV now accounts for over 70% of oropharyngeal carcinomas (OPC). More than 90% of these OPC are associated with HPV 16, one of the 14 high-risk subtypes. In recent years, HPV positive (HPV+) OPC has been one of the most rapidly rising cancers, by new cancer incidence rates, especially among men. The HPV driven OPCs present some unique infection driven cellular and molecular features, constituting distinct preventive, therapeutic and prognostic strategies, in contrast to HPV negative (HPV-) OPC. In this manuscript, we drive through the role of HPV, from a sexually transmitted infection to malignancy transformation landscape, while parsing through the significant immune response environment. In this comprehensive review we collate key literature around transmission and epidemiology of HPV, while addressing the current state of clinical management, diagnosis, prevention and screening strategies, that can help shed light on this critical area.

Risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are not well-defined in resident physicians and fellows (trainees). We aimed to identify sociodemographic, occupational and community factors associated with SARS-CoV-2 infection among trainees during the first wave of the coronavirus disease 2019 (COVID-19) pandemic in New York City (NYC). In this retrospective cohort study, we administered an electronic survey between June 26 and August 31, 2020 to trainees at the Mount Sinai Health System in NYC to assess risk factors for SARS-CoV-2 infection between February 1 and June 30, 2020. We used Bayesian generalized linear mixed effect regression and structural equation models to examine associations. SAR-CoV-2 infection was determined by self-reported IgG antibody and reverse transcriptase-polymerase chain reaction results and confirmed with laboratory results. Among 2354 trainees invited to participate, 328 (14%) completed the survey and reported test results. The cumulative incidence of SARS-CoV-2 infection was 20.1%. Assignment to medical-surgical units (odds ratio [OR], 2.51; 95% CI, 1.18-5.34), and training in emergency medicine, critical care and anesthesiology (OR, 2.93; 95% CI, 1.24-6.92) were independently associated with infection. Deployment to care for unfamiliar patient populations was protective against infection (OR, 0.16; 95% CI, 0.03-0.73). Community factors were not significantly associated with infection after adjustment for occupational factors. Our findings may inform tailored infection prevention strategies for trainees responding to the COVID-19 pandemic.

The peptide hormone kisspeptin attenuates liver steatosis, metabolic dysfunction-associated stea-tohepatitis (MASH) and fibrosis in mouse models, by signaling via the kisspeptin 1 receptor (KISS1R). However, whether kisspeptin impacts fibrogenesis in the human liver is not known. We investigated the impact of a potent kisspeptin analog (KPA) on fibrogenesis using human precision cut liver slices (hPCLS) from fibrotic livers from male patients, in human hepatic stellate cells (HSCs), LX-2 and in primary mouse HSCs. In hPCLS, 48 h and 72 h of KPA (3 nM, 100 nM) treatment decreased collagen secretion, and lowered expression of fibrogenic and inflammatory markers. Immunohistochemical studies revealed that KISS1R is expressed and localized to HSCs in MASH/fibrotic livers. In HSCs, KPA treatment reduced transforming growth factor beta (TGFb)-induced expression of fibrogenic and inflammatory markers, in addition to decreasing TGFb induced collagen secretion, cell migration, proliferation and colony formation. Mechanistically, KISS1R signaling downregulated TGFb signaling by decreasing SMAD2/3 phosphorylation, via activation of protein-phosphatases PP2A, that dehosphorylates SMAD 2/3. This study revealed for the first time that kisspeptin reverses human hepatic fibrogenesis thus identifying it as a new therapeutic target to treat hepatic fibrosis.

The emergence of antimicrobial resistance (AMR) Candida auris presents a formidable global health challenge, causing severe healthcare-associated infections with high mortality rates. Its ability to colonize surfaces and resist standard disinfectants undermines traditional hygiene practices, prompting an urgent need for novel strategies. Ultraviolet C (UVC) light offers a promising approach due to its rapid and broad-spectrum germicidal efficacy. This review comprehensively examines the current knowledge of UVC LED technology in combating C. auris, highlighting its effectiveness, limitations, and potential applications in healthcare hygiene. UVC light has potent activity against C. auris, including multidrug-resistant (MDR) strains. UVC can reduce C. auris on contaminated surfaces, aiding in transmission prevention. This review explores implementation strategies, including mobile UVC systems for targeted disinfection of high-risk areas and equipment, integration into air handling units (AHUs) to continuously disinfect recirculating air, and incorporation into water treatment systems. Current limitations in our understanding of UVC safety and effectiveness necessitate further research to optimize application protocols and ensure treatment safety while maintaining efficacy against C. auris. Integrating UVC disinfection technology into infection control programs holds promise for strengthening hygiene practices that will curb the global spread of C. auris and improve patient outcomes.

In this case study, we describe a well-resourced private school system that implemented COVID-19 mitigation measures based on public health expert guidance as well as the lessons learned from this process. Avenues is a 10-year-old private school with campuses in NYC, São Paulo, Shenzhen, Silicon Valley, as well as online, which offers education at sixteen grades: 2 early learning years, followed by a pre-kindergarten–grade 12 school. We describe the mitigation measures Avenues implemented. We compare COVID-19 case prevalence at the school to the reported case prevalence in New York City, as reported by the New York State Department of Health. We compare the school's indoor air quality (IAQ) to ambient IAQ measures reported in the literature. The school's COVID-19 transmission mitigation measures successfully reduced the prevalence of COVID-19 among its students, staff, and faculty. The school also established a consistent high level of IAQ through various ventilation mechanisms. The school received positive parent and community feedback on the policies and procedures it established, with many parents commenting on the high level of communication and trust established by the school. This successful school reopening provides useful data for school closure and reopening standards to prepare for future pandemic and epidemic events.

The healthcare sector in Kuwait has been nurtured for many decades by the government where the Ministry of Health (MoH) controls up to 80 percent of the country’s health services. General and specialized public hospitals represent a significant proportion of health expenditures in Kuwait; amidst dwindling health resources, a declining global oil market in an oil-dependent welfare state, and a heavy reliance on a non-national clinical workforce. Although healthcare services in public sector hospitals are at highly subsidized rates, causing private sector involvement in healthcare to be considerably low, the growing demands for private delivery of care burgeoned participation of private hospitals in Kuwait, and improving hospital efficiency and productivity is more critical and timelier than ever. This paper aims to analyze public health system efficiency and hospital performance in the State of Kuwait, where we begin by evaluating the input-oriented technical efficiency (TE) of MoH hospitals in 2015-2019 and identifying potential areas for efficiency improvement by exploring influencing institutional and environmental factors. Over the five years between 2015 and 2019, TE in MoH hospitals has decreased by an average of 2.98% solely based on technical regress, where the six MoH general hospitals reported a pooled mean efficiency of 86.58%, and the nine sampled MoH specialized hospitals had a five-year pooled average of 65.47% efficiency. MoH policymakers should focus on improving allocative efficiency in the public health system, and healthcare policy reforms should focus on strengthening management structures in Kuwait’s public hospitals to improve production efficiency and financial sustainability.

The paper presents a new type of hydraulic clutch operating with magnetorheological (MR) fluids and the results achieved from both theoretical analysis and experimental measurement. The hy-draulic clutch system of its own design with MR working fluid and a rotating magnetic field lo-cated is designed. The principle of this clutch is to use a rotating magnetic field created by an al-ternating current electromagnet to set the MR fluid in motion. In the tests of the hydraulic clutch with a rotating magnetic field, MR fluid produced on their own, consisting of solid iron particles of various diameters mixed with a silicone oil, are used as working fluids. The rheological properties of MR fluids are assessed on the basis of tests carried out with a Brookfield DV2T rheometer equipped with a magnetic device for generating the magnetic field of its own design. The char-acteristics of the hydraulic clutch with MR working fluid and the rotating magnetic field are tested on a specially built test stand. As a result of the research, it has been found that the torque transmitted by the clutch is the greater, the higher the rotational speed of the magnetic field and the lower the rotational speed of the beaker in which the working fluid is placed, and that the greatest torque occurs for the working fluid with the highest iron content. Based on the analysis of the structure and characteristics of the clutch in which the magnetic field is used, it has been shown that the design of the developed clutch is similar to that of an induction clutch, and its characteristics correspond to the characteristics of the eddy current clutch. Therefore, the proposed new clutch working with MR fluid and the rotating magnetic field can be applied to stationary power transmission system similarly to an eddy current clutch.

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, through the review of traditional IDS technology, it analyzes its limitations in dealing with complex network traffic. Then, the design principle of the CNN-Focal model is described in detail, which uses threshold convolution and SoftMax multi-class classification technology to effectively improve abnormal traffic detection's accuracy and efficiency. The experimental results show that CNN-Focal performs well on the open data set, demonstrating the potential and advantages of its application in the natural network environment and providing a new perspective and method for further research of deep learning in the field of network security in the future.

Amid the array of challenges prompted by the COVID-19 pandemic, the policy response of governments proved vital. The U.S. states pursued varied policy approaches that demonstrated distinct divergences in impact. Particularly, contrasting state contexts produced markedly assorted frameworks for the delivery of public health services, which indicate potential effects for COVID-19 policy implementation. Using the rates of COVID-19 vaccinations as a measure of policy execution, the purpose of this study is to examine the political and administrative influences contributing to differences in COVID-19 public health policy outcomes among the U.S. states. Ordinary least squares (OLS) regression models included data sources of nationally representative information pertaining to state public health attributes of politics and administration during the height of the pandemic and vaccine campaign. The findings suggest that state political and fiscal orientations display significant associations with COVID-19 vaccination disparities, while other administrative indicators did not. The results signal important policy implications for the politics-administration dynamic within the COVID-19 pandemic and offer further understanding toward the roles of federalism and governance in future public health emergencies.

Bat feces were reported in ancient Chinese medicine books to have the effect of reducing heat and improving eyesight, but the mechanism of vision improvement still needs further research. To this end, we used 2,2-diphenyl-1-picrylhydrazyl (DPPH) and liquid chromatography/tandem mass spectrometry (LC/MS/MS) to analyze the antioxidant capacity of and the types of vitamins in bat feces. We hoped to screen the pharmacological components of bat feces, and to explain the role that these components may play in the treatment of visual deterioration. Our results found that bat feces had a good antioxidant capacity and mainly contain vitamins B1 (thiamine), B2 (riboflavin), B3 (nicotinamide), B3 (nicotinic acid), and B5 (pantothenic acid). Although these vitamins may help maintain the health of the optic nerve and cornea, the vitamin content of bat feces is low, but the heavy metal content is high, as shown using inductively coupled plasma mass spectrometry (ICP-MS) analysis. Therefore, we suggest that the use of bat feces as traditional Chinese medicine to improve vision should be strictly restricted.

We consider creation of entanglement between the remote electron spins using a magnetic nanoparticle attached to a cantilever tip (CT). We assume that the frequency of the CT vibrations matches the Larmor frequency of the spin (the CT-spin resonance). In the conditions of the CT-spin resonance the CT-spin system is described by the exactly solvable Jaynes-Kummings model. In this work using the evolution operator of the Jaynes-Kummings model we show that a movable CT can create an entangled state between the remote spins. The most striking result is that the entanglement between the remote spins can be achieved without measurement of the vibrational state of the CT.

This paper explores the application of machine learning (ML) algorithms in predicting trends in the fixed bond market, where traditional analytical methods have proven inadequate. Focusing on various ML techniques such as supervised and unsupervised learning, neural networks, and deep learning, the study evaluates their effectiveness in forecasting market movements. It details a series of experiments in which different ML models are rigorously trained and tested against historical bond market data. The findings reveal that models employing time series analysis and advanced deep learning show marked potential in accurately predicting bond market trends. Additionally, the paper delves into the challenges and limitations inherent in these ML approaches, including data requirements and the risk of model overfitting. Finally, it proposes directions for future research, emphasizing the integration of ML into broader financial market analysis.

High systolic blood pressure and increased blood pressure variability after the onset of ischemic stroke are associated with poor clinical outcomes. One of the key determinants of blood pressure is arteriolar size, determined by vascular smooth muscle tone and vasodilatory and vasoconstrictor substances that are released by the endothelium. The aim of this study is to outline alterations in vasomotor function in isolated peripheral arteries following ischemic stroke. The reactivity of thoracic aortic segments from male C57BL/6 mice to dilators and constrictors was quantified using wire myography. Acetylcholine induced endothelium-dependent vasodilation was impaired after ischemic stroke (LogIC50 Sham=-7.499, LogIC50 Stroke=-7.350, P=0.0132, n= 19, 31 respectively). Vasodilatory responses to SNP were identical in isolated aortas in sham and stroke groups. Phenylephrine induced vasoconstriction was impaired in aortas isolated from stroke animals in comparison to sham counterparts (Sham LogEC50= -6.652 vs Stroke LogEC50=-6.475, P< 0.001). Our study demonstrates that 24 hours post-ischemic stroke, peripheral vascular responses are impaired in remote arteries. Aortas from stroke animals exhibited reduced vasoconstrictor and endothelium-dependent vasodilator responses, while endothelium-independent vasodilatory responses were preserved. Since both vasodilatory and vasoconstrictor responses of peripheral arteries are impaired following ischemic stroke, our findings might explain increased blood pressure variability following ischemic stroke.

Live-streaming commerce (LSC) is a new shopping method that combines the characteristics of social commerce and e-commerce. Since the global coronavirus disease 2019 (COVID-19) outbreak, the number of branded platforms is growing rapidly, and their competition is fiercer than ever. Understanding consumer needs and improving service quality become the key issue for survival. This study aims to develop and empirically validate a multi-dimensional hierarchical model for measuring service quality on LSC platforms. A hierarchical reflective construct was proposed to capture dimensions based on literature on e-retail and social commerce service quality. The pro-posed model was rigorously tested using two waves of survey data through the partial least squares method. Results showed that the service quality of LSC is a third-order, reflective con-struct and includes five primary dimensions (the streamer’s interaction quality, physical envi-ronment, website quality, outcome quality and ordering process) and twelve sub-dimensions (trustworthiness, expertise, responsiveness, telepresence, consumption scenarios, information quality, system operation quality, fulfillment and refund/compensation, privacy/security, contact and ease of use). Findings also supported the hypothesis that service quality has a significant impact on customers’ satisfaction and their behavioral intentions. Furthermore, we tested an al-ternative model, and the results showed that the relationship between dimensions and overall assessment is reflective rather than formative. We offered directions for further research on LSC service quality and discussed managerial implications stemming from the empirical findings.

The role that aquatic aerosols might play in inter-ecosystem exchange in freshwater riparian environments has largely been understudied. In these environments, where freshwater streams are used both as drinking water and for treated waste disposal, water features like waterfalls, downed trees, and increased streamflow can serve as bioaerosol producers. Such water features could have an important role in the bacterial colonization of surrounding surfaces, including the riparian phyllosphere. In this study, we explore the influence of a freshwater stream’s bacterial community composition and micropollution on riparian maple leaves exposed to bioaerosols produced from the stream. Using culture-based and non-culture-based techniques, we compared phylloplane microbial communities in riparian zones, adjacent non-riparian forested zones, and the surface waters of the stream. In this system, riparian zone maple leaf surfaces had higher bacterial counts than non-riparian zone trees. Using metagenomic profiling of the 16S rRNA gene, we found that, while microbial communities on leaves in both riparian zone and forested sites were diverse, riparian zone bacterial communities were significantly more diverse. In addition, we found that riparian leaf bacterial communities shared more amplicon sequence variants (ASVs) with stream bacterial communities than forest leaves, indicating that the riparian zone phyllosphere is likely influenced by bioaerosols produced from water surfaces.

This paper explores the application of machine learning in financial time series analysis, focusing on predicting trends in financial enterprise stocks and economic data. It begins by distinguishing stocks from stocks and elucidates risk management strategies in the stock market. Traditional statistical methods such as ARIMA and exponential smoothing are discussed in terms of their advantages and limitations in economic forecasting. Subsequently, the effectiveness of machine learning techniques, particularly LSTM and CNN-BiLSTM hybrid models, in financial market prediction is detailed, highlighting their capability to capture nonlinear patterns in dynamic markets. The study demonstrates the advancements in predictive accuracy and robustness achieved by deep learning methods through empirical analysis and model validation. The findings contribute significantly to academic discourse and offer practical insights for investors, financial analysts, and policymakers navigating market volatility and optimizing investment strategies. Finally, the paper outlines prospects for machine learning in financial forecasting, laying a theoretical foundation and methodological framework for achieving more precise and reliable economic predictions.

This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images, annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIB) and Partial Self-Attention (PSA) modules to enhance feature extraction and classification accuracy. Training on the Viking cluster with state-of-the-art GPUs, our model achieved remarkable results, including a mean Average Precision ([email protected]) of 98.5%. Detailed analysis of the model's performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. Even for challenging defect types such as thick line and star crack, the model maintained high performance with accuracies of 94% and 96%, respectively. The recall-confidence and precision-recall curves further demonstrate the model's robustness and reliability across varying confidence thresholds. This research not only advances the state of automated defect detection in photovoltaic manufacturing but also underscores the potential of YOLOv10 for real-time applications. Our findings suggest significant implications for improving the quality control process in solar cell production.

The leading cause of gastroenteritis in children under the age of five is rotavirus infection, accounting for 37% of diarrhoeal deaths in infants and young children globally. Oral rotavirus vaccines have been widely incorporated into national immunisation programs, but whilst these vaccines have excellent efficacy in high-income countries, they protect less than 50% of vaccinated individuals in low- and middle-income countries. In order to facilitate the development of improved vaccine strategies, a greater understanding of the immune response to existing vaccines is urgently needed. However, the use of mouse models to study immune responses to human rotavirus strains is currently limited as rotaviruses are highly species-specific and replication of human rotaviruses is minimal in mice. To enable characterisation of immune responses to human rotavirus in mice, we have generated chimeric viruses that combat the issue of rotavirus host range restriction. Using reverse genetics, the rotavirus outer capsid proteins (VP4 and VP7) from either human or murine rotavirus strains were encoded in a murine rotavirus backbone. Neonatal mice were infected with chimeric viruses and monitored daily for development of diarrhoea. Stool samples were collected to quantify viral shedding, and antibody responses were comprehensively evaluated. We demonstrated that chimeric rotaviruses were able to efficiently replicate in mice. Moreover, the chimeric rotavirus containing human rotavirus outer capsid proteins elicited a robust antibody response to human rotavirus antigens, whilst the control chimeric murine rotavirus did not. This chimeric human rotavirus therefore provides a new strategy for studying human rotavirus-specific immunity, and could be used to investigate factors causing variability in rotavirus vaccine efficacy. This small animal platform therefore has the potential to test the efficacy of new vaccines and antibody-based therapeutics.

This article explores the specific impact of artificial intelligence (AI) -driven data visualisation technologies in the user experience of the Internet industry. Through the introduction of machine learning and data mining technologies, AI has significantly improved the intelligence and automation of data visualization tools, promoting innovation in visualization methods and improving the quality and efficiency of data cleaning, processing, and analysis. In addition, AI has also promoted the development of personalised data visualization, providing users with a richer and more intuitive way to present data. This paper analyses the applications of AI-driven data visualization in machine learning, natural language processing, and computer vision. It demonstrates the positive effects of AI data visualization on user understanding and optimisation of models through practical feedback from Yellowbrick tools.

: We report the synthesis and characterization of Mn(III) chloride (MnIIICl3) complexes coordinated with N-oxide ylide ligands, namely trimethyl-N-oxide (Me3NO) and pyridine-N-oxide (PyNO). The compounds are reactive and, while isolable in the solid-state at room temperature, readily decompose into Mn(II). For example, “[MnIIICl3(ONMe3)n]” decomposes into the 2D polymeric network compound complex salt [MnII(µ-Cl)3MnII(µ-ONMe3)]n[MnII(µ-Cl)3]n•(Me3NO•HCl)n (4). The reaction of MnIIICl3 with PyNO forms varied Mn(III) compounds with PyNO coordination and these react with hexamethylbenzene (HMB) to form the chlorinated organic product 1-cloromethyl-2,3,4,5,6-pentamethylbenzene (8). In contrast to N-oxide coordination to Mn(III), the reaction between [MnIIICl3(OPPh3)2] and 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO) resulted in electron transfer forming d5 manganate of the [TEMPO] cation instead of TEMPO–Mn(III) adducts. The reactivity affected by N-oxide coordination is discussed through comparisons with other L–MnIIICl3 complexes within the context of reduction potential.

Accurate forecasting and efficient management of commodities are crucial for stakeholders in the industry, given the volatile nature of these markets. This study addresses these needs by leveraging advanced forecasting techniques and machine learning models to predict prices and enhance supply chain efficiency. TThe focus is on utilizing ARIMA, SARIMAX, and LSTM models to analyze historical trading data for commodities such as cocoa, coffee, and sugar sourced from Kaggle’s comprehensive dataset. The research applies ARIMA and SARIMAX models to forecast price trends, overcoming initial challenges related to data index frequency and seasonality. LSTM models are employed for more nuanced demand forecasting, particularly for random-length lumber, demonstrating the model's capability to predict future market trends accurately. The study highlights significant improvements in prediction accuracy and supply chain management through meticulous feature engineering and model optimization, offering valuable insights for strategic decision-making in the supply chain sector.

Accurate stock market predictions following earnings reports are crucial for investors. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates ‘base factors’, such as financial metric growth and earnings transcripts, with ‘external factors’, including recent market indices performances and analyst grades, to create a rich, supervised dataset. This comprehensive dataset enables our models to achieve superior predictive performance in terms of accuracy, weighted F1, and Matthews correlation coefficient (MCC), especially evident in the comparison with benchmarks such as GPT-4. We specifically highlight the efficacy of the llama-3-8b-Instruct-4bit model, which showcases significant improvements over baseline models. The paper also discusses the potential of expanding the output capabilities to include a ‘Hold’ option and extending the prediction horizon, aiming to accommodate various investment styles and time frames. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.

Weight-bearing computed tomography (WBCT) enables acquisition of three dimensional bony structure images in a physiological weight-bearing position, which is fundamental in under-standing the pathologic lesions and deformities of the ankle joint. Over the past decade, re-searchers focused on validating and developing WBCT measurements, which has significantly enhanced our knowledge of common foot and ankle disease. Consequently, understanding the application of WBCT in clinical practice is becoming more important to produce improved out-comes in the treatment of disease around the ankle joint. This review will describe an overview of what is currently being evaluated in the foot and ankle surgery using WBCT and where the course of research will be heading in the future.

Background: Youth experiencing homelessness (YEH) in East Asia Pacific (EAP) represent one of the most at-risk populations due to cultural and geographical factors. Effective mental health interventions, primarily researched in Western contexts, may not fully apply to YEH in EAP. Their lack of stable shelter, disrupted social networks, and limited access to mental health services elevate their susceptibility to adverse mental health, making urgent interventions essential to address their needs. Objective: To explore and provide systematic evidence on the types of mental health services and interventions for YEH in EAP and their impact on overall quality of life and wellbeing. Methods: Electronic databases(e.g., Medline, PsychInfo, PubMed, Scopus) were systematically searched(publication dates between 01/01/1990-13/05/2023), as well as additional online resources specific to homelessness. Articles were screened, and a critical appraisal assessed the quality of the included studies. Results: Eight studies with different interventions were identified in Indonesia(n = 2), Malaysia(n = 1), South Korea(n = 3), and The Philippines(n = 1). These were thematically clustered into six categories: art, cognitive behavioural therapy, life skills education, resilience enhancement, family strengthening and government interventions/services. Conclusion: This review highlights effective mental health interventions’ positive impact on YEH mental health outcomes and quality of life in EAP, stressing the urgent need to implement socio-culturally sensitive services. Future research should address knowledge gaps through comprehensive studies covering diverse EAP regions and populations, prioritising socio-culturally specific psychological measures.

In 2015 in Brazil, Zika virus showed features of geographic expansion and potentially increased virulence. In 2016, New York State issued emergency regulations after the World Health Organization declared Zika virus a Public Health Emergency of International Concern. In this study, mosquito surveillance was conducted in Westchester County, New York, to identify Zika virus and other arboviruses. Twenty trap sites were used for surveillance of Aedes albopictus mosquitoes, the Zika virus vector. The Westchester County Department of Health performed testing for Zika, West Nile, Eastern equine encephalitis, and other flaviviruses on 369 batches comprising 8,891 mosquitoes. Aedes albopictus mosquitoes were identified in Nassau, Rockland, Suffolk, Westchester, and New York City. Despite the increased capacity for specimen analysis, Zika virus was not detected. This study provides the first evidence of appropriate Zika virus surveillance. However, the results do not allow determination of the potential mechanism of local vector-to-human transmission of Zika virus among Aedes albopictus mosquitoes. This study adds to the evidence regarding the distribution, emergence, and trapping capabilities of potential Zika virus vectors.

Background: Since cytokine receptor like factor 1 (CRLF1) has been implicated in tissue regeneration, we hypothesized that CRLF1 released by mesenchymal stem cells can promote the repair of osteochondral defects. Methods: The degree of a femoral osteochondral defect repair in rabbits after intra-articular injections of bone marrow derived mesenchymal stem cells (BMSCs) that were transduced with empty adeno associated virus (AAV) or AAV containing CRLF1 was determined by morpholog-ical, histological and microCT analyses. The effects of CRLF1 on chondrogenic differentiation of BMSCs or catabolic events of interleukin-1beta-treated chondrocyte cell line TC28a2 were deter-mined by alcian blue staining, gene expression levels of cartilage and catabolic marker genes us-ing real time PCR analysis, and immunoblot analysis of Smad2/3 and STAT3 signaling. Results: Intra-articular injections of BMSCs overexpressing CRLF1 markedly improved repair of a rabbit femoral osteochondral defect. Overexpression of CRLF1 in BMSCs resulted in the release of a homodimeric CRLF1 complex that stimulated chondrogenic differentiation of BMSCs via enhancing Smad2/3 signaling, whereas the suppression of CRLF1 expression inhibited chondro-genic differentiation. In addition, CRLF1 inhibited catabolic events in TC28a2 cells cultured in an inflammatory environment, while a heterodimeric complex of CRLF1 and Cardiotrophin like Cytokine (CLC) stimulated catabolic events via STAT3 activation. Conclusion: A homodimeric CRLF1 complex released by BMSCs enhanced the repair of osteochondral defects via the inhibition of catabolic events in chondrocytes and the stimulation of chondrogenic differentiation of precursor cells.

This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance.

This research aims to investigate psychological anxiety factors, particularly Music Performance Anxiety (MPA), that influence the sustainability of music learning systems. This study employs an innovative integration of qualitative and quantitative methodologies, marking the first use of a nonlinear system paired with a comprehensive data framework for analyzing questionnaire responses. This approach allows for a detailed examination of the effects of these factors on academic outcomes. Previous research has primarily focused on the fragmented music learning system from the perspective of teachers' instructional strategies, emphasizing phased teaching methods and learning objectives which focus often neglected a significant subset of music learners. Addressing this oversight, the current study specifically centers on the emotional aspects of anxiety within music learning systems, with a particular emphasis on students, who play a crucial role in music education. This perspective enables a deep exploration of how MPA and other related emotional factors affect music learning systems. A notable innovation of this research is the development of a stochastic nonlinear system designed to model the psychological factors impacting student music learning, complemented by a quantitative model that assesses actual learning outcomes. By analyzing this nonlinear system, the research identifies weight values for various negative emotions, particularly those related to MPA, that significantly influence the music learning process. The integration of MPA and other anxiety related emotional factors with practical learning outcomes provides a comprehensive understanding of their combined impact on the music learning system. These insights are invaluable for educators and policymakers aiming to enhance both the effectiveness and emotional well-being within music education. This comprehensive approach offers a novel perspective in understanding and improving the dynamics of music education through a more empathetic and scientifically grounded lens.

In the contemporary era, youthful and healthy skin is a pivotal determinant of beauty. Choices pertaining to one's dietary and lifestyle practices wield substantial influence over skin health. Currently, the focal point of attention lies in strategies to delay skin aging and maintain skin quality. Remarkably, the skin, the body's largest organ, serves as the primary defense barrier against external elements. Skin aging encompasses intrinsic and extrinsic categories, both susceptible to genetic, lifestyle, and environmental factors. Given the strides in science and technology, the pursuit of effective and safe interventions for skin aging assumes paramount importance. Thus, this review delves into the intricate relationship between diet, lifestyle, and skin aging, culminating in an exploration of the crucial role played by excess iron in this intricate nexus. Understanding these dynamics holds promise for advancing our knowledge of skincare and the quest for timeless vitality.

This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of march 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types.

Orodispersible film (ODF) is one of the novel formulations that have been currently made available that disintegrates rapidly in the mouth without the requisite for water and offer several benefits over conventional oral solid dosage formulations. This delivery system serves as a discreet and convenient mode of administration, especially in patients who have dysphagia, fluid restriction, being beneficial to pediatric, geriatric, and bedridden patients. A novel sildenafil ODF containing sildenafil citrate is formulated to be used in patients with erectile dysfunction (ED). This review discusses the advantages of ODF in improving compliance and satisfaction in these patients and describes the bioequivalence and stability studies of sildenafil ODF. This formulation offers unique benefit to patients with ED by improving their acceptance, compliance, and respecting their privacy. Moreover, the comparison of pharmacokinetic parameters between the sildenafil ODF administered with and without water and the conventional film-coated tablet were similar. It also demonstrated reliable performance that yielded a consistent product, meeting all specifications at release and after three weeks of storage under stressed conditions (60oC). Sildenafil ODF warrants improved ease of intake, taste, portability, storage, and compliance among ED patients, making it the potential most preferred formulation and drug of choice.

Epilepsy is one of the most prevalent and serious brain disorders, affecting 70 million people worldwide. Antiseizure medications (ASMs) though relieve symptoms and prevent the occurrence of future seizures in epileptic patients have limited effect on epileptogenesis. Addressing the multifaceted nature of epileptogenesis and its association with Nod-like receptor family pyrin domain containing 3 (NLRP3) inflammasome-mediated neuroinflammation requires a comprehensive understanding of the underlying mechanisms for development of targeted therapeutic strategies beyond conventional antiseizure treatments. Several types of NLRP3 inhibitors have been developed and their effect has been validated both in vitro and in vivo models of epileptogenesis. In this review, we discuss the advances in understanding the regulatory mechanisms of NLRP3 activation as well as progress made and challenges facing in the development of NLRP3 inhibitors for the treatment of epilepsy.

This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.

This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

Cafestol, a bioactive compound found in coffee, has attracted considerable attention due to its potential impact on cardiovascular health. This review aims to comprehensively explore the association between cafestol and cardiovascular diseases. We delve into the mechanisms through which cafestol influences lipid metabolism, inflammation, and endothelial function, all of which are pivotal in cardiovascular pathophysiology. Moreover, we meticulously analyze epidemiological studies and clinical trials to elucidate the relationship between cafestol and cardiovascular outcomes. Through a critical examination of existing literature, we aim to provide insights into the potential benefits and risks associated with cafestol concerning cardiovascular health.

Lyme disease is a highly prevalent tick-borne infection caused by Borrelia burgdorferi spirochetes in North America and Eurasia. Diagnosis of the early disease remains difficult with the currently approved serological tests in the absence of noticeable tick bite or erythema migrans due to presentation of non-specific symptoms and the absence of antibodies early in infection. Furthermore, it is difficult to distinguish persistence of disease and reinfection in the endemic regions. If not treated early, infection spreads throughout the body and affects most organs with disease manifestations including arthritis, musculoskeletal pain, neuroborreliosis and acrodermatitis chronicum atrophicans. Although most patients recover after antibiotic treatment, ~10-12% of show recurrence of symptoms known as post-treatment Lyme disease syndrome (PTLDS). Various causes of PTLDS are not well-defined; however, the spirochete persisters, antigenic debris, dysregulation of immunological response in individuals or a combination of these factors are proposed to result in continuation of Lyme disease manifestations. Based on these considerations, this review highlights currently employed treatment approaches with antimicrobials and vaccines being tried for prevention of B. burgdorferi infection. We also summarize combinatorial approaches that could be employed to alleviate symptoms to improve the quality of life of the individuals suffering from long Lyme disease.

In a trip planning service, travelers first set a starting point, a destination, and a sequential list of specific points of interest types (e.g., museums, restaurants, and parks). Based on this information, the service searches the spatial database to customize the best travel itinerary for the tourist. However, in previous studies, planners only considered the time factor when designing the optimal route, and failed to fully consider the quality of each point of interest. In this study, we specifically leveraged the capabilities of large language model to parse and respond to complex travel-related user queries. To apply large language model to route planning, we fine-tuned the model to understand geotagging and user travel preferences. We have introduced a novel graph search algorithm combined with large language model output, which optimizes the route search process to provide optimal travel recommendations by taking into account various factors such as distance length, budget constraints, and popularity of tourist attractions. In addition, we have integrated real-time traffic data and historical travel data to further improve the prediction accuracy and application usefulness of the model. In the experimental validation phase, we designed a series of benchmarks to compare the performance of the system with traditional algorithms and other machine learning-based route planning methods. Experimental results show that our model has a significant improvement compared with traditional methods in improving the speed and accuracy of path selection.

Summary Genome assembly projects have grown exponentially due to breakthroughs in sequencing technologies and assembly algorithms. Evaluating the quality of genome assemblies is critical to ensure the reliability of downstream analysis and interpretation. To fulfil this task, we have developed the AssemblyQC pipeline that performs file-format validation, contaminant checking, contiguity measurement, gene- and repeat-space completeness quantification, telomere inspection, taxonomic assignment, synteny alignment, scaffold examination through Hi-C contact-map visualisation, and assessments of completeness, consensus quality and phasing through K-mer analysis. It produces a comprehensive HTML report with method descriptions, tables, and visualisations. Availability and Implementation The pipeline uses NextFlow for workflow orchestration and adheres to the best-practice established by the nf-core community. This pipeline offers a reproducible, scalable, and portable method to assess the quality of genome assemblies – the code is available online.GitHub: https://github.com/Plant-Food-Research-Open/assemblyqc Supplementary information Pipeline usage documentation, parameter descriptions and example outputs are available on GitHub: https://github.com/Plant-Food-Research-Open/assemblyqc/tree/main/docs. A preview report is also hosted online: https://plant-food-research-open.github.io/assemblyqc

The genetic causes of the differentiated, highly treatable and mostly non-fatal papillary thyroid cancer (PTC) are not yet fully known. The widely accepted PTC etiology blames altered sequence or/and expression level of certain biomarker genes. However, our previous studies found that the PTC biomarkers played significantly lower roles than the personalized PTC Gene Master Regulators. Our publicly accessible gene expression data from the cancer nodule and the surrounding normal tissue of surgically removed PTC tumor were re-analyzed to determine the transcriptomic effects on the genomic fabrics responsible for the DNA replication, repair and transcription. Tumor results were compared to the gene expression profiles of the papillary (BCPAP) and the anaplastic (8505C) human thyroid cancer cell lines. The analyses were carried out from the Genomic Fabric Paradigm (GFP) perspective that provides the most theoretically possible comprehensive characterization of the transcriptome and its alterations in disease. The study indicated that, in addition to regulating numerous genes, changes in the homeostatic control of transcript abundances and remodeling of the gene networks had major contributions to the PTC occurrence, resilience and proliferation. Therefore, the molecular mechanisms responsible for gene expression control and inter-coordination should also be considered when designing personalized anti-cancer gene therapies.

Metabolic dysfunction associated fatty liver disease (MAFLD), a globally prevalent chronic liver disease, has only limited pharmacological options. Both metformin and Ganwei, a botanical supplement containing a potent inducer for glycine N-methyltransferase (GNMT), have shown great promise for MAFLD. We conducted a 4-arm, randomized, placebo-controlled trial to investigate the safety and efficacy of metformin and Ganwei for MAFLD. Between September 2021 and March 2023, 64 patients with MAFLD were received metformin alone, Ganwei alone, combination of metformin and Ganwei, and placebo for 6 months. The primary MAFLD outcome was assessed by control attenuation parameter (CAP) and kilopascal (kPa) scores for liver steatosis and fibrosis, utilizing FibroScan. At 6 months after treatment, significant MAFLD improvements were observed in patients treated with Ganwei alone (repeated measures ANOVA test, p = 0.048 for both CAP and kPa scores). By employing a 6-point steatosis grade scale, the Ganwei alone arm exhibited a statistically better improvement than the placebo-controlled arm (mean score 1.1±1.9 vs. 0.1±0.7, p &lt; 0.01). In addition, patients in the Ganwei alone arm showed significant quality-of-life improvements. There was no serious adverse events observed. Our study demonstrated that Ganwei can be an effective treatment option for MAFLD patients. Clinical trial registration: NCT06244550.

Wastewater genomic surveillance is an efficient and economic approach to track infectious diseases and can help detect unusual epidemiological events. The current study uses this approach for the first time in Morocco to monitor the epidemiology of SARS-CoV-2 and Influenza A, B and RSV virus infections during the third wave of COVID-19 caused by the Omicron variant. The virome was concentrated from wastewater collected from two sewer sheds of two cities, Agadir and Inezgane, using the PEG/NaCl method. The viral copy number was determined using qRT-PCR. All 26 samples in both cities were tested positive for SARS-CoV-2 at various viral loads. For Influenza A virus, two samples were tested positive in Inezgane. The estimated SARS-CoV-2 viral RNA copy numbers observed were then used to estimate the number of infected individuals using the SEIR model. The number of viral copies of SARS-CoV-2 for Agadir and Inezgane varied from 82932 ± 15479 to 507865 ± 58759 copies/L and 61244 ± 8552 to 497506 ± 18061 copies/L, respectively. The estimated number of cases correlates positively with the number of reported cases. Next Generation Sequencing showed that samples contain the following two variants: BA.1 and BA.2 that have been detected in clinical samples. This study demonstrates the effectiveness and feasibility of wastewater genomic surveillance in monitoring pathogens such as SARS-CoV-2 in Morocco.

Macular pigment optical density (MPOD) is an important clinical biomarker for ocular conditions like macular degeneration, diabetic eye disease, and digital eye strain. Additionally, its measurements can be essential in health assessment for visual function, systemic diseases, and brain health. We aimed to assess the repeatability, agreement, and effects of the learning curve of the new portable handheld heterochromatic flicker photometer, Zx Pro, in measuring MPOD in a wide age range of ocular-healthy adults, compared to the MPOD measurements obtained using the clinically available QuantifEye device. Seventy-six participants performed one practice at-tempt and two study-related MPOD measurements with the Zx Pro and the QuantifEye. The Pearson correlation between the study-related MPOD measurements for Zx Pro and QuantifEye devices was 90% and 85%, respectively. Altman and Bland plots show excellent agreement between the device's MPOD data, with 95% limits of an agreement being -0.10 to +0.11 du. The mean difference between the practice attempt and the study-related measurements was not statistically significant for Zx Pro but was significant for QuantifEye (Repeated measures ANOVA p=0.325 and p=0.015, respectively). The Zx Pro provides excellent repeatable MPOD measurements, has an insignificant learning curve, and is in good agreement with the predicate device.