Last Chance Tourism (LCT) is an increasingly popular phenomenon whereby tourists seek encounters with vanishing landscapes, cultures, and endangered species. However, there are concerns that it is not sufficiently ecologically informed, has a large carbon footprint, and may put further pressure on vulnerable ecosystems and communities. The Arctic has emerged as a major global frontier for LCT, and at the same time is at the forefront of disruptive and accelerating climate change. Here a review of important LCT literature is combined with a review of key insights from the concept of Ecological Grief, which calls for explicitly mourning the losses of formerly ecologically rich landscapes, species diversity, and traditional knowledge. While there is currently insufficient engagement with grief and mourning in LCT, Ecological Grief can provide a new research focus as well as a pathway to share empathy, concern, and sorrow between scientists, communities, and visitors—thereby offering an alternative way to critically reflect upon our connections with the vanishing cryosphere in the Anthropocene.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Background: Availability of large longitudinal population health and aging studies, such as the English Longitudinal Study on Ageing, hold many opportunities for the examination of the development of Frailty over time. Lacking are psychometrically robust longitudinal measurement of the concepts of frailty required for accurate and reliable estimates and identification of frail individuals. Objectives. Development of a psychometrically robust longitudinal research tool of Frailty. The tool is to be easy to replicate for future use in health and aging research. Participants/Measurements. Data comes from four waves of ELSA (2014-2017); N=39,528. Six domains of health status deficits that may measure frailty were used: Mobility, Daily Function, Self-rated general health, Self-rated pain, Depression, Self-report doctor diagnoses of health. Design. Exploratory factor analysis (EFA) of common-self-reported deficits available across four waves of longitudinal data were compared to determine which deficits were a best fit for a research-based frailty index. Standardized regression factor scores were calculated to represent individual’s placement in factors identified from EFA. Cross-sectional validation and reliability are reported. Results. Five-Factors of Frailty found were consistent across ELSA waves. Mobility/Severe Pain, Planning/Self Care, Depression, Moderate Pain/Movement/Arthritis and Cardio-Metabolic. The variable make-up of each factor differed between males and females. Psychometric properties of the five factor frailty model were taken into account at each stage of development (factorability/sampling adequacy, cross-sectional replicability, internal consistency, low interdeterminacy, relations with age).Conclusions. Five distinct factors of an accumulation of deficits approach to frailty were found. Use and development of the five factors of frailty research tool are outlined. This research tool will aid researchers in determining the complex risk factors and outcomes of empirically derived components of frailty in ELSA.

Soybean (Glycine max L.) is a vital leguminous crop known for its rich nutritional profile, includ-ing high protein and oil content, making it a valuable resource in global agriculture and human nutrition. In Pakistan due to regulatory issues non-GMO soybean is grown therefore, the universal herbicide control method is not applicable. Similarly, for other crops, specific herbicides are avail-able but for soybean no crop specific herbicide is present. A comprehensive study was conducted over two growing seasons, in 2022 and 2023, at Agronomic Research Farm University of Agri-culture Faisalabad. The research involved 16 different treatments, including both six pre-emergence and eight post-emergence herbicides as well as a control group and weedy check. Findings of this study showed that critical importance of weed management in soybean cultiva-tion, as unchecked weed infestation can lead to substantial yield losses. Among the pre-emergence herbicide S-Metolachlor + Pendimethalin performed best and showed higher plant height (54.33 cm & 53.67 cm), 100 grain weight (11.57 g & 12 g), seed yield plant-1 (13.07 g and 12.87 g), lower Susceptibility Index (10%), higher weed control percentage (91.21% & 95.65%) higher weed con-trol efficiency (92.02% & 93.43%), herbicide efficiency index (27.54% & 32.48%) and lower weed index (18.16% & 21.70%) during 2022 and 2023 respectively. However, in post emergence herbi-cide the Fluizefop-p-butyl is most effective herbicide due to less crop injury 10% Susceptibility In-dex, higher plant height (42.67 cm & 44 cm), whole plant weight (93.33 g & 95 g), 100-grain weight (11.40 g & 11.77 g), seed yield plant-1 (12.87 g & 13.13 g), higher weed control percentage (91.12% & 91.03%) higher weed control efficiency (89.73% & 92.36%, herbicide efficiency index (21.18% & 30.41%) and lower weed index (19.42% & 20.08%) in both 2022 and 2023 respectively. Notably, herbicides such as S-Metolachlor + Atrazine + Mesotrione, Topramezone, Mesosulfu-ron-methyl + Iodosulfuron-methyl sodium and Mesotrione + Atrazine led to complete plant mor-tality, resulting lethal to soybean. The findings of this study support the hypothesis that screened the best suitable herbicides with less plant injury, and higher efficiency index. So, it is recom-mended that the application of S-Metolachlor + Pendimethalin as PRE and Fluizefop-p-butyl as POST showed the least injury and best weed control results with higher grain yield.

Abstract: Canine ehrlichiosis is a zoonotic disease transmitted by ticks, posing a formidable challenge to both veterinary and public health sectors worldwide. Across continents and regions, the prevalence rates of Ehrlichia canis exhibit significant variation, underlining the necessity for a nuanced and globally informed approach to understanding and combating this disease. The review navigates through this complexity, shedding light on the intricate pathogenesis of the illness. Central to this understanding is the bacterium's adept manipulation of the host's immune response, contributing to the diverse clinical manifestations observed in infected animals. Diagnostic methodologies, crucial for timely intervention and management, are subjected to critical assessment. From traditional microscopy to modern molecular techniques and serology, each approach is scrutinized for its strengths and limitations. By acknowledging these nuances, the review aims to equip practitioners with the knowledge necessary to make informed diagnostic decisions. A central tenet of this review is the advocacy for an integrated "One Health" approach. By leveraging advancements in genomics, proteomics, and artificial intelligence, there is a potential to enhance diagnostic accuracy and develop innovative therapeutic and preventive strategies globally. This collaborative approach recognizes the interconnectedness of human, animal, and environmental health, offering a holistic framework for tackling complex zoonotic diseases like canine ehrlichiosis. In conclusion, this review serves as a beacon of knowledge, illuminating the multifaceted landscape of canine ehrlichiosis. Through its synthesis of scientific literature and emphasis on methodological rigor, it provides a foundation upon which future research and interventions can be built. With a unified commitment to "One Health" principles and the integration of cutting-edge technologies, there exists the potential to mitigate the impact of this disease and safeguard both animal and human well-being worldwide

Switzerland, a wealthy country, has a cutting-edge healthcare system, yet per capita, it emits over one ton of CO2, ranking among the world's most polluting healthcare systems. To estimate the carbon footprint of the healthcare system of Geneva’s canton, we collected raw data on the activities of its stakeholders. Our analysis shows that when excluding medicines and medical devices hospitals are the main greenhouse gas emitter by far, accounting for 48% of the healthcare system’s emission, followed by nursing homes (20%), private practice (18%), analysis laboratories (7%), dispensing pharmacies (4%), the homecare institution (3%) and the ambulance services (

Background/Objectives: Endometriosis represents substantial direct and indirect healthcare costs impacted by absent accurate, non-invasive uniformly diagnostic tools. We endeavored to demonstrate gastrointestinal myoelectrical activity (GIMA) biomarkers unique to endometriosis allow noninvasive, uniformly accurate diagnosis or exclusion of endometriosis. Methods: Prospective open-label comparative study of 154 patients, age >18, with or without diagnosed endometriosis. Population included 62 non-endometriosis controls (Cohort 1), 43 subjects with surgically histologically-confirmed endometriosis (Cohort 2), and 49 subjects with abdominal pain and negative imaging (Cohort 3). Electroviscerography (EVG) recorded GIMA biomarkers from three abdominal electrodes before and 30-minutes post water-load protocol. Cohort 2 had postoperative EVG and Cohort 3 had preoperative EVG. Calculated specificity, sensitivity, NPV, PPV and predictive probability or C-Statistic used univariate, multivariate, linear and logistical regression analyses of the area under the curve (AUC) at all frequency and time points, including age and pain covariants. Results: Non-endometriosis cohort differed significantly from endometriosis cohorts (p<0.001) for median (IQR), and AUC percent frequency distribution of power at baseline, 10-minute, 20-minute, and 30-minute post water-load at all frequency ranges 15-20cpm, 30-40cpm and 40-50cpm. Endometriosis cohorts were statistically similar (p>0.05). GIMA biomarker threshold scoring demonstrated 95%/91% sensitivity and PPV, 96%/95% specificity and NPV and C-statistic of >99%/98% respectively for age subsets. GIMA biomarkers in Cohort 3 predicted 47/49 subjects positive and 2/49 negative for endometriosis, confirmed surgically. Hormonal therapy, surgical stage, nor pain score affected diagnostic accuracy. Conclusions: Non-invasive electroviscerography with GIMA biomarker detection distinguished participants with and without endometriosis based upon endometriosis specific GIMA biomarkers threshold scoring.

Today, there are commercially available high-performance electric wheelchairs, capa-ble of responding to strong limitations. However, despite these advancements, there is a significant portion of the population with severe disabilities who cannot access this mobility solution due to safety reasons. Indeed, either due to motor issues preventing reliable control of the wheelchair's commands, or associated visual or cognitive pathologies, the use of an electric wheelchair by these individuals would put them at risk and could also constitute a danger to those around them. This is why our research team is working on the development of augmented wheelchairs that allow a greater autonomy. We are mainly developing a system compatible with all electric wheel-chairs on the market, capable of making them anti-collision and anti-fall on uneven ground (sidewalks, stairs, etc.). In this article, we present our solution regarding the anti-collision function, in particular the sensors used to do this, as well as the strategy. We will focus on the shortcomings and weaknesses of various types of sensors and demonstrate how, by combining them in a certain way, we can develop a collision avoidance device capable of managing the diversity of everyday life situations. The two types of sensors that will be combined here are an in-house developed special near infrared sensor from the laboratory and a commercially available ultrasonic sensor. We will present the internal operation of the sensors, their limitations, experimental tests involving integration with the wheelchair, and the structure of the complete device.

Data4HumanRights training materials have been developed as open-source and tailored to limited resource settings where community data collectors often live and work. An overview of how the training material was developed together with community data collectors in Nigeria and Kenya is provided. The paper gives insights into the fundamental principles (i.e., inclusiveness, adaptive, limited resources, gender- and incentive-sensitive) and the structure of the open-access training materials. This resulted in 28 modules designed to be delivered in a face-to-face format in less than one day by a local trainer. To maximize adaptivity, the training modules can be mixed and matched (e.g., as individual modules or a learning path of several modules around a specific training need). The individual modules cover a range of methods and tools that are useful to human rights work, for example, documenting evictions, performing rapid needs assessments after acute crises, community profiling, and monitoring community development indicators. The training materials contain instructions for the facilitator(s) of the training and all necessary training materials to conduct the training. To maximize inclusivity, the training covers basic to advanced topics. Most training modules address basic training topics that can be optimally followed with a mobile phone (to avoid using a computer and printing handouts). The training was developed and implemented with local community groups in Nigeria and Kenya. The material is free to use, adapt, and translate into different contexts and languages.

Objective: Diabetes disproportionately affects African Americans leading to higher morbidity and mortality. This study explores the experiences of African Americans adults who successfully self-manage their type 2 diabetes (called Peer Ambassadors) and provided phone-based peer support in a 6-month culturally tailored diabetes self-management program for African Americans guided by the Information-Motivation-Behavioral skills model. Design: A group discussion using a semi-structured discussion guide was conducted. Qualitative content analysis was used to identify the facilitators and barriers to completing the role of a Peer Ambassador, and to develop strategies for overcoming possible challenges in the future. Setting: Key informant discussions were conducted in a community location to gain insights into Ambassadors’ motivations and challenges in delivering peer support. Participants: Three Peer Ambassadors completed ethics training, peer mentor training and received a phone call guide before providing support to their peers. Results: There were four core themes related to Peer Ambassador experiences: (1) Motivation to be a Peer Ambassador, (2) Program elements that supported Peer Ambassador role, (3) Key elements of achieving engagement, and (4) Challenges related to being a Peer Ambassador. Conclusions: This study showed Peer Ambassadors in a culturally tailored peer-supported self-management program found fulfillment in sharing experiences and supporting peers. They highly valued educational group sessions for knowledge updates and sustaining their health-related goals, suggesting the potential benefits of recognizing milestones or providing advanced training for future program sustainability. Findings suggest the importance of recruiting motivated patients and providing effective facilitation for peer support roles, including addressing barriers such as time commitment and lack of socialization opportunities.

The COVID-19 pandemic crisis has brought about unprecedented economic and social consequences worldwide. The implementation of isolation measures, including national and international travel restrictions, significantly affected contact-intensive sectors such as tourism. During this lockdown period, Information and Communication Technology (ICT) was crucial in maintaining connectivity and enabling remote activities. Governments across the world implemented discretionary measures to mitigate the crisis's adverse effects on economic activity. This paper aims to analyze whether the level of ICT adoption and the importance of tourism influence the fiscal policy efforts pursued by governments. Based on the results of a linear regression analysis for 154 countries, the findings suggest that in countries with higher international tourism receipts, the governments had to promote higher fiscal stimulus. At the same time, ICT development diminished the efforts of the fiscal policy response to the crisis since it facilitated economic resilience. The findings of this study can contribute to understanding how governments responded to the pandemic crisis and the factors that influenced their fiscal policy decisions.

Recent advancements in machine learning have ushered in innovative techniques for augmenting datasets, particularly through contrastive learning in the computer vision domain. This study pioneers the application of contrastive learning for sentiment analysis, introducing a novel approach termed EmoConLearn. By fine-tuning contrastive learning embeddings, EmoConLearn significantly surpasses BERT-based embeddings in sentiment analysis accuracy, as evidenced by our evaluations on the DynaSent dataset. This research further delves into the efficacy of EmoConLearn across various domain-specific datasets, highlighting its versatility. Additionally, we investigate upsampling strategies to mitigate class imbalance, further enhancing EmoConLearn's performance in sentiment analysis benchmarks.

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSD) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support to the prevention of back pain or the development of MSD. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, Skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal, valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures and cognitive load detection from physiological signals.

COVID-19 pandemic disproportionately affected seven South Asian countries because of rampaging virus and weak health system response. Vaccination commenced in all countries during early 2021, once the vaccines became available in the international market. Vaccines were donated by international development partners, friendly countries and procured utilizing domestic resources and financial support from various agencies. All countries opted for a phasing of the vaccination by prioritizing high risk populations which included health care personnel, front line workers and elderly with comorbidities. Seven vaccines that had WHO emergency authorization usage were utilized in South Asia. India produced more than 2.4 billion doses of AstraZeneca vaccine and has indigenously developed five COVID-19 vaccines. Vaccine inequity was obvious and characterized by fewer people in South Asia vaccinated with single dose vis a vis the developed countries. Vaccine hesitancy was another obstacle that kept the vaccination rates low. Difficult to reach areas in Afghanistan, Bhutan, Maldives, Nepal could not fully access vaccines. South Asian countries need to strengthen their health system for efficient deployment of vaccines in future health emergencies. At the same time the global agencies and developed countries must address the issue of vaccine access to low-income countries to mitigate the impact of pandemic all over the world.

Space-based gravitational wave detection is extremely sensitive to disturbances. The Keplerian configuration cannot accurately reflect the variations in spacecraft configuration. Planetary gravitational disturbances are one of the main sources. Numerical simulation is an effective method to investigate the impact of perturbation on spacecraft orbits. This study shows that, in the context of the Taiji project, Earth’s gravity is an essential factor in the change in heliocentric formation configuration, contributing to the relative acceleration between spacecrafts in the order of O(10−6)m·s−2. Considering 00:00:00 on October 27, 2032 as the initial orbiting moment, under the influence of Earth’s gravitational perturbation, the maximum relative change in armlengths and variation rates of armlengths for Taiji is 1.6×105km, 32m·s−1 respectively, compared with the unperturbed Keplerian orbit. Additionally, by considering the gravitational perturbations of Venus and Jupiter, the armlength and relative velocity for Taiji are reduced by 16.01% and 17.45%, respectively, compared with when only considering that of Earth. The maximum amplitude of the formation motion indicator changes with the orbit entry time. Results show that the relative velocity increase between the spacecrafts is minimal when the initial orbital moment occurs in July. Moreover, the numerical simulation results are inconsistent when using different ephemerises. The differences between ephemeris DE440 and DE430 are smaller than those between DE440 and DE421.

Analysis of immediate effects of the COVID-19 pandemic on SMEs in Rwanda aims to assess and quantify the economic and financial impacts of the pandemic during the lockdown and presents options adopted by SMEs to reopen their business opera-tions. It also seeks to recommend options for sustainable and resilient economic recovery from the scars of COVID-19. This analysis used data collected from nearly 220 SMEs located across the country and applied an Exploratory Data Analysis (EDA) and multivariate linear regression models to measure the pandemic effects. The study revealed that firms downsized employment by 36.4 per cent, but data depict significant deviation within SMEs by their size. The COVID-19 pandemic has had a greater magni-tude of negative effects on sales levels among small businesses. Tax amounts were reduced amidst the crisis however, data do not show any significant variations among SMEs. Additionally, SMEs owners applied various coping mechanisms during re-opening. The study used firm-level data, providing detailed and reliable effects of the shocks on business performance. This analysis recommends establishing the medium-term financing and technical support for SMEs to ensure steady and sustainable recovery from pandemic effects as well as strengthening their resilience to any other socio-economics shocks.

Probiotic feed additives have attracted considerable research interest in recent years because the effectiveness of probiotics can differ across microbial strains and the supplemented macroorganisms. The present study was conducted on 16 lambs divided into two groups (C - control and E - experimental). The examined lambs were aged 11 days at the beginning of the experiment and 40 days at the end of the experiment. The diet of group E lambs was supplemented with a multi-strain probiotic formulation (Lactobacillus plantarum AMT14, Lactobacillus plantarum AMT4, Lactobacillus rhamnosus AMT15, and Bifidobacterium animalis AMT30), whereas group C lambs did not receive the probiotic additive. At the beginning of the experiment (day 0) and on experimental days 15 and 30, blood was sampled from the jugular vein to determine and compare: phagocytic activity (PHAGOTEST) and oxidative metabolism (PHAGOBURST) of peripheral blood granulocytes and monocytes by flow cytometry. An analysis of the phagocytic activity of granulocytes and monocytes revealed significantly higher levels of phagocytic activity (expressed as the percentage of phagocytic cells and mean fluorescence intensity) in lambs administered the multi-strain probiotic formulation than in the control group. The probiotic feed additive also exerted a positive effect on the oxidative metabolism of both granulocytes and monocytes (expressed as the percentage of oxidative metabolism and mean fluorescence intensity) after stimulation with Escherichia coli bacteria and with PMA (4-phorbol-12-β-myristate-13-acetate).

Respiratory syncytial virus (RSV) infection is a major cause of severe respiratory disease in infants and young children worldwide. Two vaccines targeting the elderly and pregnant women have been recently approved employing the prefusion form of the RSV-fusion protein (F). However, there are currently no vaccines for infants. Studies have shown a fusion-inactive prefusogenic F form is significantly more immunogenic and produces higher antibody titers to both the prefusion and postfusion structures of the F protein. Here we have developed RSV virus-like particles (RSV-VLPs) consisting of prefusogenic F, RSV glycoprotein and matrix proteins, produced them using the baculovirus expression system, and studied their protective efficacy in Balb/c mice. Morphology and successful assembly of VLPs were confirmed by transmission electron microscopy and western blot. Mice immunized with VLPs developed high levels of serum IgG and neutralizing antibodies as compared with mice immunized with inactivated virus. The VLP vaccine also induced higher levels of IFNγ and IL4, elicited limited proliferation of CD4+ and CD8+ T cells but higher cytotoxic-T-lymphocyte responses. VLP immunization abolished lung pathology in the mice after RSV challenge. Overall, our results indicate that RSV-VLPs consisting of prefusogenic F, glycoprotein and matrix proteins are a potential vaccine candidate against RSV.

The number of engineers who are transitioning into environmental sustainability careers is growing, though a gap still exists between supply and demand. This presents an opportunity for undergraduate engineering students to fulfill the demand as environmental sustainability professionals. This qualitative exploratory study investigated environmental sustainability learning experiences and future career interests in environmental sustainability. The social cognitive theory (SCCT) was utilized as a theoretical lens, exploring undergraduate students’ environmental sustainability interests, related learning experiences and their interest in pursuing a future career in environmental sustainability. Twenty-five undergraduate engineering students in various engineering disciplines were interviewed for this study. Data were analyzed to (1) identify students’ interest in pursuing a career in environmental sustainability, (2) determine if students’ interests have changed since they began their undergraduate studies, and (3) explore how learning experiences have impacted students’ future career choice. The findings posit that exposure to environmental sustainability learning experiences is impactful and plays an important role, impacting students' interests in pursuing careers in sustainability. Results reveal that elements such as personal beliefs and salary considerations inspire career choices. This research contributes to addressing the demand for additional working professionals who are prepared to tackle environmental sustainability issues, highlighting the role of learning experiences in shaping students' career interests.

Background: The Coronavirus Disease 2019 (COVID-19) pandemic presented a new challenge in modern medicine. The development of vaccines was followed by massive population vaccinations. A few reports on post-vaccination allergic reactions have made patients and medical personnel uneasy as to anti-COVID-19 vaccines’ allergising potential. Most of the studies in this area to date have been small, and some that were based on global databases skipped most of the allergic diseases and concentrated only on anaphylaxis. We aimed to analyse the incidence of serious allergic reactions based on the EudraVigilance (EV) database, regardless of the reported symptoms and allergy mechanism. Methods: The total number of administrated vaccine doses was extracted on the 5th October 2023 from Vaccine Tracker and included all administrations since vaccinations began in the European Economic Area (EEA). Data on serious allergic reactions to COVID-19 vaccines was extracted from the EudraVigilance database with the same time point. The code names of 147 allergic symptoms or diseases were used. Results: The frequency of serious allergic reactions per 100,000 administered vaccine doses was 1.53 for Comirnaty, 2.16 for Spikevax, 88.6 for Vaxzevria, 2.11 for Janssen, 7.9 for Novavax, 13.3 for VidPrevtyn Beta and 3.1 for Valneva. The most prevalent reported reactions were oedema (0.46) and anaphylaxis (0.40). Only 6% of these reactions were delayed hypersensitivity-oriented. Conclusions: The overall frequency of potential serious allergic reactions to COVID-19 is very rare. Therefore, COVID-19 vaccines seem to be safe for human use. The lowest frequency of allergic reaction was observed for Comirnaty and the highest for Vaxzevria.

Sarcopenia is associated with clinical complications, which increase mortality. Effective sarcopenia risk screening in the elderly should be grounded in a comprehensive assessment, encompassing both nutritional status and medication use. Current screening tools, while emphasizing muscular per-formance, exhibit limited sensitivity in identifying elderly individuals at risk of sarcopenia. This cross-sectional study aimed to develop a sarcopenia risk screening tool, named Sarc-Global, with enhanced sensitivity, incorporating factors pertinent to the overall health of the elderly. Utilizing the criteria established by EWGSOP2 for diagnosing sarcopenia, we evaluated 395 communi-ty-dwelling elderly individuals. This evaluation served as a reference for assessing the efficacy of two validated questionnaires, Sarc-F and Sarc-CalF, and our newly developed Sarc-Global. Con-structed through multiple logistic regression, Sarc-Global integrates variables such as sex, age, medication use, body mass index, arm circumference, and handgrip strength – all significantly associated with sarcopenia (p

VITEK MS PRIME (bioMerieux), a newly developed Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) system, alongside VITEK PICKME Pen (PICKME), offers easy sample preparation for bacteria and yeast. VITEK MS PRIME also offers two software platforms for filamentous fungi: the IVD database and the RUO database. Our study evaluated its identification agreement on 320 clinical isolates of bacteria and yeasts, comparing PICKME and traditional wooden toothpick sampling techniques against ASTA Mi-croIDSys Elite results. Additionally, we assessed the IVD (v3.2) and SARAMIS (v4.16) RUO data-bases on 289 filamentous fungi against molecular sequencing. The concordant rates for spe-cies-level identification of bacteria and yeasts were about 89.4% (286/320) between the PICKME and wooden toothpick, about 83.4-85.3% between VITEK MS PRIME and ASTA MicroIDSys Elite. Retesting with PICKME improved concordance to 91.9%. For filamentous fungi, species-level identification reached 71.3% with the IVD database and 85.8% with RUO, which significantly enhanced basidiomycetes' identification from 35.3% to 100%. Some strains in the IVD database, like Aspergillus vesicolor, Exophiala xenobiotica, and Nannizzia gypsea failed to be identified. VITEK MS PRIME with PICKME offers reliable and efficient microorganism identification. For fila-mentous fungi, combining the use of the RUO database can be beneficial, especially for basidi-omycetes.

A novel concept of ion separation Anisotropic Diffusion Mobility Analyser (ADMA) with Individual Ion Counter (IIC) was shown by numerical modelling to be able to separate ions with high resolution and ultra-high sensitivity. An ADMA-IIC device has been developed and tested at sheath flow rates below 40 l/min with a number of chemicals relevant to airport security and military applications. The resolving power of the ADMA was observed to be in the range of 40 to 80. These data were close to the resolution obtained in numerical modelling predictions. It is greater than or comparable with the RP of currently available on-the-market devices for airport security and other applications. It was also confirmed that the ADMA technology enables very fast detection of analytes, at least 100 times faster than ion mobility spectrometers. It was shown that Brownian diffusion can be controlled and even overpowered by generating highly anisotropic diffusion in the ADMA device. The ultimate sensitivity of single-ion detection below the ppq range has been demonstrated.

An easy to implement and cost-effective Fiber Bragg Grating (FBG) sensor interrogation technique based on a ring Erbium-Doped Fiber Laser (EDFL) topology is proposed and experimentally assessed. The FBG sensor is part of the EDFL cavity and must have a central wavelength located within the linear region of the EDF’s amplified spontaneous emission (ASE) spectrum which occurs between 1530 and 1540nm. In this manner, the wavelength-encoded response of the FBG under strain is converted to a linear variation of the laser output power, removing the need for spectrum analysis as well as any limitations from the use of external edge-filtering components. In addition, the laser linewidth is significantly reduced with respect to the FBG bandwidth thus improving the resolution of the system, whereas its sensitivity can be controlled through the pumping power. The performance of the system has been characterized by modeling and experiments for EDF's with different lengths, doping concentrations and pumping power levels. The influence of mode-hopping in the laser cavity on the resolution and accuracy of the system has also been investigated.

The release of the active substance from the matrix modified-release tablets based on Kollidon®SR and chitosan depends on its solubility in the dissolution medium and on the composition of the matrix-forming polymers. The study is based on new oral matrix tablets based on Kollidon®SR and chitosan, formulated to optimize the low oral bioavailability of chlorzoxazone, a non-steroidal anti-inflammatory drug, a class II Biopharmaceutical Classification System. Nine types of chlorzoxazone matrix tablets were obtained using the direct compression method, and their combination at 1:1, 1:2, 1:3 chlorzoxazone/ excipients, 20%-40% (w/w) Kollidon®SR, 3-7% (w/w) chitosan and the auxiliary substances were formulated in constant concentrations. Pharmaco-technical characterization included the analysis of flowability and compressibility properties (flow time, friction coefficient, angle of repose, Hausner ratio, Carr Index), and of pharmaco-chemical characteristics (mass and dose uniformity, thickness, diameter, mechanical strength, Friability, degree of softening, in vitro release profile) of the tablets. Based on the initial evaluation of the pharmaco-technical analysis, pharmaco-chemical characteristics, and in vitro release profile, only three matrix tablet formulations (F1b, F2b, and F3b) were further tested. They were evaluated through Fourier-transform infrared spectrometry, X-ray Diffraction, thermogravimetry, differential scanning calorimetry, and in vitro dissolution tests. The three formulations were comparatively studied regarding the release kinetics of active substances using in vitro release testing. The results were analyzed by fitting into four representative mathematical models for the modified release oral formulations. In vitro kinetic study reveals a complex mechanism of release occurring in the first (0 - 2 hours) and second (2.0 hours – 36 hours) steps of drug release. Two factors have been calculated to assess the release profile of chlorzoxazone: f1 - the similarity factor and f2 - the factor difference. The results have shown that both Kollidon®SR and chitosan may be used as matrix-forming agents when combined with chlorzoxazone. In vitro dissolution tests revealed the F2b sample, data suggesting a faster drug release. The three formulations showed optimum pharmaco-technical properties and in vitro kinetic study, and they had tremendous potential to be used in oral pharmaceutical products for the controlled delivery of chlorzoxazone.

Input noise causes inescapable bias to the weight vectors of the adaptive filters during the adaptation processes. Moreover, the impulse noise at the output of the unknown systems can prevent bias compensation from converging. This paper presents a robust bias compensation method for a sparse normalized quasi-Newton least-mean (BC-SNQNLM) adaptive filtering algorithm to address this issue. We have mathematically derived the biased-compensation terms in an impulse noisy environment. Inspired by the convex combination of adaptive filters' step sizes, we propose a novel variable-mixing-norm method to accelerate the convergence for our BC-SNQNLM algorithm, which is referred to as BC-SNQNLM-VMN. Simulation results confirm that the proposed method significantly outperforms other comparative works regarding normalized mean-squared deviation (NMSD) in the steady state.

Parasomnias are a group of sleep disorders characterized by abnormal and unpleasant motor, verbal or behavioral events that occur during sleep or during transitions between wake and sleep states. They disrupt sleep and can have a detrimental impact on the individual experiencing them. Our goal was to identify types of parasomnias and their prevalence in the post-secondary student population and to explore students' coping strategies for parasomnias they found distressing. Seventy-seven post-secondary students completed the 21-item Munich Parasomnia Screening (MUPS) frequency scale. They also rated, on a 10-point scale, how disturbing each parasomnia was. Not only did 92% percent of students report at least one parasomnia, but our results also indicate that the vast majority of students experienced more than one parasomnia. This led us to investigate the likelihood of the co-occurrence of different parasomnias. With respect to the level of subjectively experienced distress, the most prevalent parasomnias were not always the more disturbing ones. Coded open-ended responses about what students do about the disturbing parasomnias indicate that grounding strategies and physical manipulation of one’s body were the most common, although most participants indicated that in spite of distress, they do nothing to cope. In conclusion, our study found a strikingly high prevalence of parasomnias in this sample of young adults and a lack of knowledge about effective means of dealing with these.

Background: The pandemic caused by the SARS-CoV-2 virus demonstrated the importance of prevention through a healthy diet and lifestyle, the most vulnerable people being those with severe chronic conditions, those who are overweight, and those with an unbalanced immune system. Methods: The evaluation of the eating habits and lifestyle of the Romanian population in the post-pandemic period was carried out based on a cross-sectional observational study with the help of a questionnaire. Results: 4704 valid answers were registered (3136 female and 1568 male respondents). Among the respondents, most of them belong to the young population, 2892 between the ages of 18 and 40, i.e., 61.5%. Most male respondents are overweight (1400) and obese (780). Most respondents indicated a tendency to consume 1-2 meals per day irregularly (p=0.6167). Only 974 respondents adopted a healthy diet, and 578 a healthy lifestyle. Conclusion: The present study reports low adherence to a healthy diet (20.7%) and healthy lifestyle (12.28%), especially among the young population (< 30 years). In the current context, it reports a reduced tendency to consume vegetables and fruits among the population, below the daily average recommended by the nutrition guidelines, a tendency towards sedentary behavior, and even deficient hydration of some of the respondents; these negative aspects can create a long term a series of nutritional and psycho-emotional imbalances. Our results evidence that complex surveys among the population are regularly required to investigate nutritional or lifestyle deficiencies; moreover, it could be helpful in further educational measures in nutrition, food, and environmental safety.

Weight loss solutions are on the rise, and there is growing interest in natural alternatives to the pharmaceuticals in the market, which are not devoid of relevant side effects. To this end, the objective of the current study is to assess the effects of a botanical-based blend comprised of lemon verbena and hibiscus extracts in weight management, body fat, appetite, lipid, and glucose profiles, in an overweight population, for 90 days. As a result, the volunteers presented significant weight loss in absence of diet, which coincided with decreased body fat. Body fat was assessed by anthropometry, electrical bioimpedance and DEXA scan. Cholesterol levels were also significantly decreased. Satiety assessment revealed that the product contributed to increased feeling of fullness and satisfaction after a meal, compared to the placebo group, which could contribute to the weight loss observed. Blood glucose and blood pressure levels remained unchanged, which was expected as the volunteers were normoglycemic and their blood pressure was in the healthy range. In conclusion, daily supplementation of a botanical-based blend contributes to weight loss in overweight individuals, in absence of diet, while not affecting other parameters that are in the healthy range. This product could be a candidate alternative or add-on to other weight loss products, as part of a healthy lifestyle.

The aim of our study was a comprehensive comparative assessment of the effect of consumption of 1 and 2% cholesterol as part of a standard diet with and without regular moderate physical activity on the main biomarkers of lipid and cholesterol metabolism, as well as the intestinal microbiota of male Wistar rats. The animals were randomly divided into 5 groups according to body weight and Open Field test results: Control (n=10), 1% Chol (n=10), 2% Chol (n=10), 1% Chol + RUN (n=10) and 2% Chol + RUN. The cholesterol was additionally added in the amount of 1g or 2g/100g, respectively, to the diet of animals. The rats of experimental groups 1% Chol + RUN and 2% Chol + RUN were subjected to regular (2 days a week) physical activity on a Treadmill. In rats of four experimental groups, when compared to the Control, a response of 39 indicators was revealed. The largest number of changes in biomarkers - 29 (statistically significant, p

It is essential to have a thorough knowledge of the genetic variation among different strains of Xanthomonas citri pv. citri (Xcc), which is responsible for causing citrus bacterial canker. This understanding is important for studying the disease’s characteristics, population structure, and evolution, and ultimately for developing sustainable methods of control. A total of 48 strains, obtained from citrus production areas in Burkina Faso in 2012, 2020, and 2021, were subjected to polymerase chain reaction (PCR) tests using specific primers. The aim was to examine the distribution of type 3 effectors (T3E) and determine the geographical origins of the strains. The examination of the distribution of type 3 non-transcription activate like effectors (TALE) indicates a broader range in strains obtained in 2020 and 2021 compared to those obtained in 2012. However, all strains possess a shared set of three genes, specifically XopE2, XopN, and AvrBs2. Furthermore, all examined effectors were observed in the Bobo-Dioulasso region. Regarding the characterization of TALEs, two profiles containing two to three TALEs were discovered. Profile 1, consisting of two TALEs, was found in 37 Xcc strains, whereas Profile 2, comprising three TALEs, was detected in 11 strains. Out of the three TALEs (A, B, and C) that were identified, TALEs B and C were present in all strains. The correlation matrix indicated a positive association between the T3E content of strains and the duration of their isolation. Principal component analysis revealed a limited organization of the strains under investigation

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Towards that goal we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant spikerbox, a device that measures changes in voltage differentials of plants between roots and leaves. Using machine learning, we find that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems, while also paving the way to use plants as biosensors for analyzing human movement.

In this essay, I revisit Neural Networks (NNs) through the lens of evolutionary dynamics. From the two most important features of NNs, I recover the two most general equations of evolutionary dynamics. These two equations may thus serve as a connection between NNs and a body of knowledge that has been built over multiple centuries and has found application across a vast range of disciplines. This connection may therefore also help explain why NNs with the two features in question are applicable across a much broader range of domains than initially envisioned.

Efficient heat management and control in optical devices, facilitated by advanced thermo-optic materials, is critical for many applications such as photovoltaics, thermal emitters, mode-locked lasers, and optical switches. Here, we investigate the thermo-optic properties of 2D graphene oxide (GO) films by precisely integrating them onto microring resonators (MRRs) with control over the film thicknesses and lengths. We characterize the refractive index, extinction coefficient, thermo-optic coefficient, and thermal conductivity of for the GO films with different layer numbers and degrees of reduction, including reversible reduction and enhanced optical bistability induced by photo-thermal effects. Experimental results show that the thermo-optic properties of 2D GO films vary widely with the degree of reduction, with significant polarization anisotropy, enabling efficient polarization sensitive devices. The versatile thermo-optic response of 2D GO substantially expands the scope of functionalities and devices that can be engineered, making it promising for a diverse range of thermo-optic applications.

Yeast two-hybrid approaches, which are based on fusion proteins that must co-localise to the nucleus to reconstitute the transcriptional activity of GAL4 have greatly contributed to understand the nitrogen interaction network of cyanobacteria, whose main hubs are the trimeric PII and the monomeric PipX regulators. The bacterial two-hybrid system, based on the reconstitution in the E. coli cytoplasm of the adenylate cyclase of Bordetella pertussis, should provide a relatively faster and presumably more physiological assay for cyanobacterial proteins than the yeast system. Here we use the bacterial two-hybrid system to gain additional insights into the cyanobacterial PipX interaction network while simultaneously assessing the advantages and limitations of the two most popular two-hybrid systems. Comprehensive mutational analysis of PipX and bacterial two-hybrid assays were performed to compare outcomes between yeast and bacterial systems. We detected interactions previously recorded in the yeast two-hybrid system as negative as well as a “false positive”, the self-interaction of PipX, which is rather an indirect interaction dependent on PII homologs from the E. coli host, a result confirmed by Western analysis with relevant PipX variants. This is, to our knowledge, the first report of the molecular basis of a false positive in the bacterial two-hybrid system.

Opuntia ficus-indica (L.) Mill. belongs to the Cactaceae family and the genus Opuntia; it is a succulent plant that adapts to extreme climatic conditions. The aerial part of the plant consists of the cladodes, morphological changes of branches that appear green, are covered with thorns, and are essential to reduce excessive perspiration of water. The composition of cladodes is very varied, and the main constituents are water, fibers, polysaccharides, proteins, fatty acids, vitamins, sterols, minerals, and polyphenols. Polyphenols are responsible for many beneficial activities for human health, such as antioxidant, anti-inflammatory, antitumor, and nutritional properties. The purpose of this manuscript was to compare the properties of cladodes belonging to the same plant but with different stages of maturity. Relative extracts were tested both in vitro and on a cell line and antioxidant and anti-apoptotic properties were found. The antioxidant activity was tested by the ORAC test, the DPPH test, and the measurement of cellular accumulation of ROS. Anti-apoptotic activity was evaluated by the annexin/PI assay and measurement of caspases 9 and 3 expression. The results obtained have shown that the extracts considered possess antioxidant and anti-apoptotic properties. However, the different stages of maturity of cladodes are essential for the performance of both functions. In addition, important variations have been made in the dissolution of extracts that have brought greater safety in their use. In conclusion, this manuscript provides further information on cladodes of Opuntia ficus-indica, which can be used as adjuvants in many human pathologies.

Misfolding of superoxide dismutase-1 (SOD1) is a pathological hallmark of amyotrophic lateral sclerosis (ALS) with SOD1 mutations. The development of antibodies specific for misfolded SOD1 deepens our understanding of how the protein participates in ALS pathogenesis. Since the term “misfolding” refers to various disordered conformers other than the natively folded one, which misfolded species are recognized by specific antibodies should be determined. Here, we molecularly characterized the recognition by MS785-MS27, an antibody cocktail experimentally confirmed to recognize over 100 ALS-linked SOD1 mutants. Indirect ELISA revealed that the antibody cocktail recognized Zn-deficient wild-type and mutated SOD1 species. It also recognized conformation-disordered wild-type and mutated SOD1 species, such as unfolded and oligomeric forms, but had less affinity for the aggregated form. Antibody-reactive SOD1 exhibited cytotoxicity to a motor neuron cell model, which was blocked by Zn treatment with Zn-deficient SOD1. Immunohistochemistry revealed antibody-reactive SOD1 mainly in spinal motor neurons of SOD1G93A mice throughout the disease course, and the distribution after symptomatic stages differed from that of other misfolded SOD1 species. This suggested that misfolded SOD1 species exist as heterogeneous populations. In conclusion, MS785-MS27 recognizes various misfolded SOD1 lacking the incorporated Zn ion and should be useful for monitoring Zn availability in SOD1.

Theoretical studies on the generation of nondiffracting and nondispersive light pulses and their experimental implementation are one of the renowned problems within electromagnetics. Current technologies enable creation of short-duration pulses of a few cycles with high power and fluency. An application of these techniques to the field of nondiffracting and nondispersive pulses requires a proper mathematical description of highly focused vector pulses. In this work, we study vector optical bullets in a dielectric medium with different polarization structures: linear, azimuthal, and radial. We report the differences caused by the vector model compared to the scalar model. We analyze effects caused by superluminal, subluminal, or even negative group velocity on properties of vector optical bullets inside a dielectric material.

The social- and self-directed behaviours of two golden lion tamarins (Leontopithecus rosalia) was investigated while having residence in the tropical house of Aalborg Zoo, Denmark. The behaviour of the female had been distinct from the male since 2018. Despite several diagnostic examinations and medicinal treatments, the cause of the aberrant and apparently stereotypical behavioural patterns remained undetermined, which initiated this study. The pair were recorded throughout a predetermined observation period and these recordings were analysed to illuminate the behaviours of the individuals. The results showed that there were differences in the type, frequency, and durations of both social- and self-directed behaviours between the sick female and the healthy male. The female expressed both social- and self-directed behaviours more frequently and for longer durations, while the male were more socially reserved and did not engage in social behaviours as frequently. Furthermore, subsequent necropsy of the female revealed uro-genital disease.

An Indo-French campaign was conducted during May 2014 at Gadanki (13.5^oN, 79.2^oE), India, for investigating gravity wave (GW) coupling between the lower and middle atmospheres using a suite of experimental techniques. In this paper we are presenting the sources for those GWs which are observed at airglow layer (~87 km) using (OH) Airglow Imager during the campaign. For this purpose we have developed a background model atmosphere by combining the ERA-Interim, MSIS E 90 model and HWM 07 model data sets. Uniqueness of the present study lies in using near-real time background atmosphere data from simultaneous radiosonde and SVU Meteor Radar covering both source and propagation/ dissipation regions of GWs. We have observed 17 wave events and used GROGRAT model for reverse tracing to locate the source(s). When we searched for the sources near the terminal points or along the ray path, deep convection is found to occur but no strong wind shear. This indicates that the source for the observed GWs is deep convection. We have also simulated small scale waves that can reach mesosphere during different seasons (background conditions). GWs with periods less than 100 minutes and horizontal wavelengths less than 200 km are mostly reaching the mesospheric altitudes. Interestingly, it is found that only few GWs that are generated in the troposphere reach the mesospheric altitudes during the monsoon season, though strong sources like convection and wind shear persist throughout this season. Thus, background wind plays significant role in deciding the propagation of GWs in to upper atmosphere.

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

In the dynamic landscape of E-commerce, the proliferation of user-generated queries regarding products highlights the critical need for advanced automatic question answering (AQA) systems. These systems are instrumental in harnessing the vast array of information available online to deliver immediate and informative responses to potential buyers. Recognizing the complexity of such queries, which often require synthesis of reviews, product specifications, and responses to similar questions, we introduce a sophisticated model, the Comprehensive E-commerce Answer Generation System (CEAGS). This model adeptly navigates the challenges posed by irrelevant data and sentiment ambiguity in user-generated content. Our approach leverages a dual-phase process of relevance determination and sentiment clarity, setting the stage for a transformative response generation mechanism. Empirical analyses reveal the superiority of CEAGS, with our relevance assessment framework outstripping existing models by a notable margin in precision metrics, and our answer generation module achieving unprecedented gains in content preservation and coherence. Notably, CEAGS marks a pioneering contribution to the E-commerce domain by integrating disparate information sources to formulate responses that are not only accurate but also contextually rich and user-centric.

Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it's vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital 'self-instruction' and 'self-inspection'. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, key for energy-efficient and Nearly Zero-Energy Buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.

In higher education, the cultivation of research competency is pivotal for students’ critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students’ statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilized the supervised machine learning approach to predict students’ research competency, as represented by their performance in a research methods class, with predictors of students’ proficiency in statistical topics. Predictors relating to students’ learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills— can be used to predict students’ research competency, as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students’ research competency. Findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Fermented foods and ingredients, including fermentates derived from lactic acid bacteria (LAB) in dairy products, can modulate the immune system. Here we describe the use of reconstituted skimmed milk powder to generate novel fermentates from Lactobacillus helveticus strains SC232, SC234, SC212, and SC210, and from Lacticaseibacillus casei strains SC209 and SC229, and demonstrate using in vitro assays that these fermentates can differentially modulate cytokine secretion by bone marrow derived dendritic cells (BMDCs) when activated with either the viral ligand, loxoribine, or an inflammatory stimulus, lipopolysaccharide. Specifically, we demonstrate that SC232 and SC234 increase cytokines IL-6, TNF-α, IL-12p40, IL-23, IL-27 and IL-10, and decreased IL-1β in primary bone marrow derived dendritic cell (BMDCs) stimulated with a viral ligand. In contrast exposure of these cells to SC212 and SC210 resulted in increased IL-10, IL-1β, IL-23, and decreased IL-12p40 following activation of the cells with the inflammatory stimulus LPS. Interestingly SC209 and SC229 had little or no effect on cytokine secretion by BMDCs. Overall, our data demonstrates that these novel fermentates have specific effects and can differentially enhance key immune mechanisms that are critical to viral immune responses, or can suppress responses involved in chronic inflammatory conditions, such as Ulcerative Colitis (UC), and Crohn’s disease (CD).

The paper presents the prediction of hydrodynamic characteristics of different rudder -plane configurations behind a submarine in deep water through the Reynolds-Averaged Navier-Stokes method in Ansys Fluent Solver. The hydrodynamic force and moment acting on the Cross-plane, X-plane, and Y-plane rudder configurations are analyzed for estimating hydrodynamic coefficientsfor predicting the advantages of each configuration to control the ship. The obtained results show that X-force of the X-plane rudder configuration is larger than the force acting on the Cross-plane rudder and Y-plane rudder configurations. However, Y-force, and Z-force of the X-plane rudder configuration are significantly greater than the Y-force of the left configurations. The same tendency can be seen in the moment of the X-plane rudder about the y-axis and z-axis. The roll moment induced by the Y-plane and Cross-plane rudder configurations is significantly larger than the case of the X-plane rudder configuration.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

This study develops a vitamin C controlled-release system trackable via color changes as a function of vitamin C release. The system is composed of coaxial microfibers prepared via coaxial electrospinning, with a core of poly(ethylene oxide) (PEO) incorporating vitamin C, and a shell composed of polycaprolactone (PCL) containing polydiacetylene (PDA) as the color-changing material. The shell thickness was controlled by adjusting the amount of PCL ejected during electrospinning, allowing the regulation of the release rate of vitamin C. When vitamin C added to PEO penetrated the PCL layer, the color of PDA changed from blue to red, indicating a color change. The results of this study can be applied to devices that require immediate detection of vitamin C release levels.

Many people in the USA have lost their lives or become addicted to opioids via prescription opioids given for chronic pain. The chronic pain epidemic has emerged due to a convergence of factors, including the medicalization of pain as well as injuries happening in the workplace, in the home, or during recreation. Opioid prescriptions rose precipitously from the late 1990’s through to approximately 2020, and despite public awareness, still are increasing. This review examines literature on integrative approaches to chronic pain as delineated by pain type. The review is organized by the most common causes of chronic pain, and randomized controlled trials, meta-analyses and systematic reviews are extracted for each cause of chronic pain. Several promising interventions may alleviate chronic pain of some causes, and some interventions may work across pain causes. Lowering inflammation through dietary or lifestyle regimens may be a general way of reducing pain. Pain can be alleviated through several adjunctive and integrative treatment approaches, which may serve to lower the need for opioid medication.

Morphological and phylogenetic (45S nrDNA+RPB1 gene) analyses of three glomoid spore-producing arbuscular mycorrhizal fungi revealed the presence of two new species be-longing to the genus Glomus in the family Glomeraceae. In the field, Glomus highlandensis sp. nov. was found in a saltmarsh of the Scottish Highlands and in maritime sand dunes of the Baltic Sea in Poland, both saline environments, while G. mongioie sp. nov. originates from the Italian Alps. Phylogenetic placement analysis using environmental sequences indicated that G. highlandensis could have likely worldwide distribution while G. mongioie seems to be a rare species in the world. The molecular and phylogenetic analysis provided important insights about the presence of ribosomal variants in Glomus, with potential negative implication in phylogeny and species recognition.

In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in the 5th Generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs) such as user experience, quality of service (QoS), or other important aspects of the network. To address this challenge, advanced wireless technologies have been incorporated into the design of 5G networks across various network layers. Given the rapid advancements in Artificial Intelligence (AI) and emerging technology trends such as Machine Learning (ML), which is considered a subset of AI, the integration of such trends into 5G networks has become a significant area of research. The primary objective of this survey is to analyze AI integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.

Integrated disaster risk reduction in schools represents a key component of safety strategies within the educational sector of every country. In this study, a series of 850 face-to-face interviews (650 with students and 200 with teachers) were conducted throughout 2023 in 10 out of the total 18 municipalities in the Western Morava Basin of the Republic of Serbia. The paper proposes two central hypotheses regarding school-based disaster risk reduction. Firstly, it suggests that gender, age, parent's employment, academic achievement, living situation, paternal and maternal education levels, and engagement with social media collectively influence students' perspectives on this matter. Secondly, it posits that gender, age, marital status, parenthood, and educational background significantly impact teachers' viewpoints on school-based disaster risk reduction. Multivariate linear regression was used to explore predictors of students' and teachers' insights on school-based disaster risk reduction. Various statistical tests including Chi-square, t-tests, one-way ANOVA, and Pearson's correlation were employed to investigate the influence of demographic and socioeconomic factors on these insights. The results of multivariate regression analyses indicate that age, gender and marital emerge as the primary predictors across various facets of students' and teachers' insights on school-based disaster risk reduction (awareness of disasters, disaster education activities, attitudes on disaster risk reduction education, enhancement of disaster information accessibility, etc). The results of this study provide insight into the key factors influencing students' and teachers' perceptions of disaster risk reduction in schools. As such, they can serve as a basis for improving educational programs, developing policies and strategies, normative educational frameworks, teacher training, and further research in disaster education.

Congenital heart disease (CHD) is a prevalent congenital disorder diagnosed with high frequency, affecting approximately 0.8% to 1.2% of all live births. Research has demonstrated that the use of pulse oximetry and clinical examination as a screening tool for CHD diagnosis has proven extremely valuable. We conducted a retrospective longitudinal study over a 3-year period that involved screening 1188 newborns admitted to the NICU during the first 72 hours of life within this timeframe. 201 newborns were diagnosed with CHD. Almost all (n=198) had a positive screening test during the 72-hour period, while 3 had a negative screening test, but were later diagnosed using echocardiography. The prevalence rate was 16.67%. A confusion matrix was constructed with 198 true positives (TP), 3 false negatives (FN), no false positives (FP), and 987 true negatives (TN). The sensitivity of the screening test was 98.51%, while the specificity 100%, with no false positives. In our study, a total of 9 deaths occurred. Using the Kaplan-Meier method, the survival probability appeared to be slightly above 0.9 (95.5%), indicating that almost all patients survived past 72 hours after being diagnosed with CHD. Pulse oximetry is an effective, low cost and easy-to-use method and a useful tool to assess and lead to the diagnosis of CHD.

The adoption of environmental conservation agriculture (ECA) in Nepal is aligned with the country's goal to achieve carbon neutrality by 2045, as ECA practices have been proven to effectively reduce greenhouse gas emissions. Nepal's agricultural sector faces numerous challenges, including labor shortages, climate change impacts, and the necessity for environmentally friendly farming methods, making the adoption of ECA practices even more crucial. This paper thus explored farmer perspectives on the sustainability of ECA practices in Namobuddha municipality, Nepal, which is renowned as a leading hub of organic farming. A cross-sectional survey was conducted, together with key informant interviews and onsite observations. By analyzing various farmer perspectives, the study presents an analytical framework that highlights the economic, environmental, and social pillars of ECA's sustainability. The findings underscore the significance of economic viability for farmers, as damages to crops and farm products negatively drive their perception of ECA sustainability. Conversely, factors such as increased agriculture-related income, favorable prices, and sustainable productivity positively shape farmers' perceptions. In terms of environmental sustainability, farmers prioritize enhancing the local and global environment, viewing their farming methods as climate-smart and actively working towards reducing greenhouse gas emissions. The study emphasizes the importance of strategic communication to effectively convey the benefits of ECA to rural communities. Overall, this research contributes to filling the knowledge gap concerning farmers' perceptions of ECA sustainability. The insights gained from this study have the potential to inform policy decisions and promote the widespread adoption of environmentally friendly farming practices in Nepal.

An imbalance between production and excretion of amyloid β peptide (Aβ) in the brain tissues of Alzheimer’s disease (AD) patients leads to Aβ accumulation and formation of noxious Aβ oligomers/plaques. A promising approach to AD prevention is reduction of free Aβ level by directed enhancement of Aβ binding to its natural depot, human serum albumin (HSA). We previously demonstrated the ability of specific low-molecular-weight ligands (LMWLs) of HSA to improve its affinity for Aβ. Here we develop this approach by bioinformatic search for the clinically approved AD-related LMWLs of HSA, followed by classification of the candidates according to the predicted location of their binding sites on HSA surface, ranking of the candidates, and selective experimental validation of their impact on HSA affinity for Aβ. The top 100 candidate LMWLs were classified into the five clusters. The specific representatives of the different clusters exhibit dramatically different behavior, with 3- to 13-fold changes in equilibrium dissociation constants for the HSA-Aβ40 interaction: prednisone favors HSA-Aβ interaction, mefenamic acid shows the opposite effect, while levothyroxine exhibits the bidirectional effects. Overall, the LMWLs of HSA chosen here provide a basis for drug repurposing for AD prevention, and for search for the medications promoting AD progression.

Spinal cord stimulation (SCS) is a therapeutic modality for the treatment of various chronic pain conditions that has rapidly evolved over the past 50 years. Unfortunately, over time, patients implanted with SCS undergo a habituation phenomenon leading to decreased pain relief. Consequently, discovery of new stimulation waveforms and SCS applications have shown to prolong efficacy and reduce explantation rates. This article explores various SCS waveforms, their applications and proposes a graded approach to habituation mitigation. We suspect the neural habituation phenomenon parallels that seen in pharmacology. Consequently we urge further exploration of early introduction of these stimulation strategies to abate spinal cord stimulation habituation.

Smart manufacturing encompasses a category of manufacturing that employs computer-integrated capabilities and technologies to enhance supply chain optimization, production efficiency, and recyclability. Yet limited studies have been conducted to optimize these prospects from the perspective of low resource countries where such innovative studies have not been adequately explored. This paper aims to examine the implications of smart manufacturing practices (Smart Procurement, Smart Supply Chain, Smart Production Planning and Control, Automation and Industrial Robot, and Supply Chain Configuration) on pro-environmental behaviour and develop a baseline moderated mediation model to explain the relationship between smart manufacturing and pro-environmental behaviour with a role of green dynamic capability, and environmental orientation. This study is anchored on quantitative research approach, deductive reasoning, and explanatory design. Cross – sectional survey data have been obtained from 382 manufacturing companies in Ghana. Our hypotheses have been tested using Structural Equation Modelling and SMART-PLS version 3.8.9. The study has revealed that smart manufacturing practices significantly and positively relate to pro-environmental behaviour. Moreover, green dynamic capability and environmental orientation moderate the relationship between smart manufacturing and pro-environmental behaviour. The implications of the study include the emergence of a baseline model to guide policymakers, practitioners, and academicians to explain the relationship between smart manufacturing and sustainable manufacturing in the context of developing countries. Again, the social implication of the study includes the realisation of decent job and economic growth, responsible consumption and production as well as actions taken to combat climate change.

Perceived peer support is a critical factor in the academic adjustment of higher vocational college students. While previous studies have explored this relationship, few studies have delved into the underlying mechanisms. This study surveyed 9782 students from 35 higher vocational colleges in 13 provinces and cities in China to explore the relationship and mechanisms between perceived peer support and academic adjustment. Drawing on Conservation of Resources theory and Social Support Resource theory, the findings revealed that perceived peer support not only directly predicted the academic adjustment of higher vocational college students, but also indirectly through the independent mediating effect of academic hope and professional identity, as well as the chain-mediating effect of academic hope to professional identity. Additionally, students from vocational high schools showed a more significant increase in academic hope due to perceived peer support compared to those from general high schools, highlighting the moderating effect of educational background. The study provided a new theoretical perspective for understanding the complex mechanisms of academic adjustment in students and offers valuable guidance for educational practice, emphasizing the importance of fostering perceived peer support, academic hope, and professional identity in higher vocational education.

Hybrid Renewable Energy Systems (HRES) integrate renewable sources, storage, and optionally conventional energies, offering an eco-friendly solution to fossil fuels. Microgrids (MGs) bolster HRES integration, enhancing energy management, resilience, and reliability at various levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010-2023, analyzed with SciMAT software. It segments the research into two periods, 2010-2019 and 2020-2023, revealing a surge in MOO focus, especially in the latter period, with a 35% increase in MOO-related research. This indicates a shift towards com-prehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of Artificial Intelligence (AI) in optimizing HMGS within the MOO framework signals a move towards more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.

Soil extracellular enzyme activity (EEAs) and enzymatic stoichiometry (ES) can provide a crucial indication of changes in soil ecosystem's nutrient availability and the microbial resource limitations. However, the changing characteristics of soil EEAs and ES at different stages of the native succession process and their key drivers are unclear. In order to investigate the soil EEAs, ES and driving factors of soil under vegetation at different succession stages, we adopted the "spatio-temporal substitution" method to collect the surface soil of bryophyte community, herbaceous community, shrub community and tree community in the new volcanic lava platform of Wudalianchi Volcanic Nature Reserve. We measured seven soil EEA, including carbon(C)-acquiring enzyme (β-1,4-glucosidase (BG)), N-acquiring enzymes (β-N-acetyl-glucosaminidase (NAG) and leucine aminopeptidase (LAP)) and phosphorus (P)-acquiring enzyme (acid phosphatase (AP)) activities. The length and angle of vectors defined by ratios of enzyme activities (BG/(NAG + LAP) vs. BG/AP) were used to indicate relative microbial investments in C- (length), and N- and P- (angle) acquiring enzymes. Our results showed that the contents of TC, TN, TP, MBC, DOC and NO3-N in shrub community soil were significantly higher than those in bryophyte, herb and tree communities, and increased by 441%, 246%, 137%, 5570%, 12% and 484%, respectively. The highest soil EEA of C-, N- and P-acquiring were found in shrub community, and the soil EEAs/MBC of C-, N- and P-acquiring were the highest in bryophyte community. Enzyme C:N, C:P and N:P ratios increased progressively in the order of bryophyte, herb and shrub community, but the enzyme C:N, C:P and N:P ratios of tree community were both far less than shrub community. Vector lengths increased progressively in the order of bryophyte (1.16), herb (1.27), tree (1.29) and shrub (1.40), and Vector angles decreased progressively in the order of bryophyte (49.15°), herb (45.65°), Tree (45.31°) and shurb (44.54°), suggested that as succession progresses, soil microbial nutrients transforms from P limitation (angle＞45°) to N limitation (angle＜45°). Redundancy analysis showed that TC, TN, EC and C:N were important drivers of variation in soil EEAs and ES in vegetation at different succession stages. Our findings highlight that the primary succession process cause nutrient limitation transformation. Soil ES might be a sensitive indicator mediated by soil microorganisms to the relative resource limitation at different stages of the primary succession process.

Experimental studies on the development of processes of obtaining ultradispersed metal-ceramic materials and coatings Mo-ZrO₂, Mo-Al₂O₃, W-ZrO₂, W-Al₂O₃ CVD by deposition from the gas phase in CO₂ process using low-temperature nonequilibrium plasma were carried out.The structure, microhardness, phase composition, and high-temperature annealing behavior of the materials were studied. It is shown that the choice of parameters of the deposition process and temperature annealing can be controlled by the distribution function of ultradispersed particles in the material and their sizes.High-temperature X-ray studies of "molecular" two-phase alloys Mo- Al₂O₃ and Mo- ZrO₂show that, depending on the elastic and thermal properties of the matrix materials and dispersed particles, the molybdenum matrix can be in an all-round tensed or all-round compressed state.In the study of the electro-physical characteristics of the two-phase disperse-strengthened alloys Mo- ZrO₂, Mo- Al₂O₃, W- ZrO₂, W- Al₂O₃ it has been established that in the region of temperatures 1023...1273K anomalous changes of specific electrical resistance and instability of thermal eddy in magnitude are observed apparently connected with the presence of internal stresses in material caused by ultradisperse particles.Theoretical aspects of the problem of increasing the carriers density in nanocomposite materials are reviewedTheoretical analysis of the effect of the ensemble of microparticles on the change of electron density in ultradisperse metal-ceramic materials is carried out. It is shown that the average values of the electron density in the matrix with the introduction of 1% of particles with a radius of 50 A increase by more than two orders of magnitude.The possibilities of creating ultradisperse compositions using radiation-plasma methods are considered.

Aluminum, a contentious trace element found in the environment, has been demonstrated to have harmful effects on both humans and animals. In contrast, Sempervivum tectorum, an evergreen plant, has been found to offer numerous beneficial effects for both humans and animals. Therefore, this study aims to assess the protective effect of S. tectorum on certain reproductive biomarkers in male rats exposed to aluminum. Thirty-five Wistar rats were randomly divided into five groups: NTC (no-treatment control) received distilled water; NC (negative control) received drinking water containing 1 mg/L aluminum sulfate (AS); E1 received 1 mg/L AS along with an 8% S. tectorum extract; PC (positive control) received only 8% S. tectorum extract; E2 received 1 mg/L AS for three months followed by 8% extract for one month. The study analyzed testosterone, LH, FSH, body weight, and the histological structure of the testis, epididymis, and prostate, as well as the levels of zinc, manganese, copper, and iron in these organs. Significant decreases in body weight, testis, and epididymis size were observed in the aluminum-exposed groups compared to the control, whereas these decreases were not significant in the S. tectorum-treated groups compared to the control. Aluminum exposure led to significant decreases in testosterone and LH levels, with FSH levels showing a non-significant decrease in males, which were mitigated significantly by the administration of the plant extract. Histological analysis revealed alterations in the testis, epididymis, and prostate of the AS-exposed groups, including necrosis of seminiferous tubule epithelium and Leydig cells in the testis, and basal epithelial necrosis in the epididymis and prostate. Aluminum levels increased in all organs studied, while levels of zinc, copper, iron, and manganese decreased, showing a negative and significant correlation with aluminum levels. The aqueous extract of S. tectorum demonstrated a protective effect on certain studied biomarkers in male rats affected by aluminum exposure.

This article deals with special curves similar to those known in mathematics as Lamé curves. Their equations have a certain similarity with the equations of Lamé curves. But these curves are more like ellipses in appearance. Like ellipses, they have major and minor axes. These are such curves that their major and minor semi-axis coincide with an ellipse but are different than an ellipse. The main feature of these curves is that for any triangle whose base coincides with the axis of a given curve and the vertex is on a given curve, the condition aⁿ+bⁿ=cⁿ is obtained. The number “n” is called the degree or rank of this curve. To distinguish this group of curves, we called them Elbe curves. The equations of these lines are also provided in the paper.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

The high reliable correlations between CBR and DCP in general proposed are presented in this paper. We analyzed the test data and correlations from many researchers to find the best equation for prediction the CBR from DCP test. The findings show that all correlations from existing paper are based on local material and cannot apply to soil in a different location in general purpose. New correlations between CBR and DCP were proposed and validated in this paper for general uses. The validation confirmed that these new correlations can predict well with higher R2.

Artery of Percheron Infarct (AOP) is a rare congenital anatomical vascular variant supplying the paramedian thalamus and the rostral midbrain. It arises from either proximal segment of the posterior cerebral artery (P1). The symptoms of an AOP infarction might vary, although they typically manifest as memory loss, altered mental status, and supranuclear vertical gaze palsy. Most of the time, the initial CT scan fails to diagnose the AOP infarct. Most of these diagnoses are made when ischemic stroke patients are not eligible for thrombolytic therapy. We report a case of a 40-year-old female with a history of well-managed hypertension, who presented in the ER with chief complaints of altered behavior, altered gaze palsy, and drowsiness. Initially, the CT scan did not show pathological changes. The diagnosis was made the next hospital day by MRI. Our case presents an unusual presentation and the lack of evidence of AOP infarction on a CT scan does not exclude its diagnosis. An infarcted Percheron artery requires a thorough clinical and radiological examination.

Rice, as a staple crop feeding billions, faces constant threats from various diseases jeopardizing global food security. Precise understanding of disease resistance mechanisms is crucial for developing resilient rice varieties. Traditional genetic mapping methods, such as QTL mapping, provide valuable insights into the genetic basis of diseases. However, the complex nature of rice diseases demands a holistic approach to gain accurate knowledge of it. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, enable a comprehensive analysis of biological molecules, uncovering intricate molecular interactions within the rice plant. Integration of various mapping techniques using multi-omics data has revolutionized our understanding of rice disease resistance. By overlaying genetic maps with high-throughput omics datasets, researchers can pinpoint specific genes, proteins, or metabolites associated with disease resistance. This integration enhances the precision of disease-related biomarkers with better understanding of their functional roles in disease resistance. Improvement of rice breeding for disease resistance through this integration represents a significant stride in agricultural science because better understanding of the molecular intricacies and interactions underlying disease resistance architecture leads to more precise and efficient development of resilient and productive rice varieties. In this review, we explore how the integration of mapping and omics data can give a transformative impact on rice breeding for enhancing disease resistance.

Melioidosis is an endemic infectious disease caused by Burkholderia pseudomallei bacteria, which contaminates soil and water. To better understand the environmental changes that have contributed to melioidosis outbreaks, this study used spatiotemporal analyses to clarify the distribution pattern of melioidosis and the relationship between melioidosis morbidity rate and local environmental indicators (land surface temperature, normalised difference vegetation index, normalised difference water index) and rainfall. A retrospective study was conducted from January 2013 to December 2022, covering data from 219 sub-districts, with each exhibiting varying morbidity rate of melioidosis on a monthly basis. Spatial autocorrelation was determined using local Moran’s I, and the relationship between melioidosis morbidity rate and the environmental indicators was evaluated using geographically weighted Poisson regression. The results revealed clustered spatiotemporal patterns of melioidosis morbidity rate across sub-districts, with hotspots predominantly observed in the northern region. Furthermore, we observed a range of coefficients for the environmental indicators, varying from negative to positive, which provided insights into their relative contributions to melioidosis in each local area and month. These findings highlight the presence of spatial heterogeneity driven by environmental indicators and underscore the importance of public health offices implementing targeted monitoring and surveillance strategies for melioidosis in different locations.

The methods of Artificial Intelligence (AI) have been used in the planning and operation of power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI including the use of Machine Learning (ML) to the power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way considering the supervised and unsupervised learning methods. Based on this, three complex application examples: wind power generation forecasting, smart grid security as-sessment (using two methods), and automatic system fault detection are presented and discussed in detail. Summary and outlook conclude the paper.

Improving the quality of petroleum products and refining processes through the use of artificial intelligence and machine learning techniques is the topic of this article. It shows that expert knowledge and conventional empirical models can't get you where you want to go in a refining process. To effectively capture complex interactions and forecast fuel qualities, machine learning techniques such as principal component analysis (PCA), support vector machines (SVM), artificial neural networks (ANN), and partial least squares (PLS) are suggested. Gasoline and other petroleum products, as well as property prediction, process control, product quality, and operational efficiency in refineries, can all be improved with the help of machine learning applied to spectral or distillation curve data. An exciting new direction in optimizing operations, meeting environmental norms, and precisely estimating gasoline quality is offered by advanced machine learning algorithms.

Viscoelastic relaxation measurements on Stirene-Butadiene rubbers (SBR) doped with Carbon Nanotube (CNT) at different concentration show the time-temperature superposition (TTS). This process is described in terms of the Mode Coupling Theory (MCT) approaches to the viscoelasticity by considering the frequency behavior of the loss modulus E”(ω), and shown that the corresponding TTS is linked to a (ω)1/2 decay. From of the analysis of the obtained data we observe that the interaction between the SBR and CNT determine different decays different decays according to their concentration. The rubbers with the lowest CNT concentration are only characterized, in the studied T-range, by a fragile glass forming behavior. Whereas those at the highest ones instead show, at a specific temperature TL, a crossover towards a pure Arrhenius, that according to the MCT, indicates the presence of a kinetic glass transition (KGT); where the system response functions are characterized by scaling behaviors.

Natural tourist destinations stand out as the most preferred choice in Rembang Regency compared to cultural and artificial tourist destinations. However, until now, there have been no standard indicators established to assess the potential for natural tourism in Rembang Regency, which could assist the Culture and Tourism Office in determining potential tourist locations. In this research, the grouping of natural tourism potential indicators will be carried out based on six main aspects, namely Attractions, Amenities, Accommodation, Activities, Accessibility and Ancillary Services, together with environmental aspects and Human Resources. The procedure for determining natural tourism potential will be developed from the results of this grouping using a taxonomic model. Indicators and the weight of each indicator will be determined using the hierarchical analysis method. The results of this research will produce a taxonomy model that can overcome challenges in managing and utilizing information and knowledge, which in turn will facilitate the process of sharing and utilizing information. Furthermore, the procedures developed will enable accurate and efficient determination of natural tourism potential in accordance with established procedures. This research method will also enable the development of a comprehensive knowledge management system at the Rembang Regency Culture and Tourism Service, through the next stages in the designed taxonomy model. This research is expected to contribute to the sustainable management of natural tourism, which can contribute to the preservation of nature while improving the tourist experience.

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. Fruit Harvest Helper seeks to simplify their process. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. The old and new applications were designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app utilizes a color ratio-based image segmentation algorithm implemented in OpenCV C++ to detect apples in apple tree images selected for processing. The results of testing the algorithm with a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture, nearing readiness for farmers to use in yield monitoring and farm management.

We report a novel dual-band barrier infrared detector (DBIRD) design using type-II superlattices (T2SLs). The DBIRD structure consists of back-to-back barrier diodes: a "blue channel" (BC) diode which has an nBp architecture, an n-type layer of larger bandgap for absorbing blue band infrared/barrier/p-type layer, and a "red channel" (RC) diode which has an pBn architecture, a p-type layer of smaller bandgap for absorbing red band infrared/barrier/n-type layer. Each has a unipolar barrier using a T2SL lattice matched to a GaSb substrate to impede the flow of majority carriers from the absorbing layer. Each channel in the DBIRD can be independently accessed with a low bias voltage, as is preferable for high-speed thermal imaging. Device modeling of DBIRDs and simulation results of the current-voltage characteristics under dark and illuminated conditions are also presented. They predict that dual-band operation of the DBIRD will produce low dark currents and 27-33% quantum efficiencies for the in-band photons in the BC with λc=5.58 μm, and a nearly-constant 33% in the RC with λc=8.05 μm. The spectral quantum efficiency of the BC for 500 K blackbody radiation is approximately 20% over the range of λ=3 − 4.2 μm, while that of the RC has a peak of 42% at 5.7 μm. The DBIRD may provide improved high-speed dual-band imaging in comparison with NBn dual-band detectors.

Neurodegenerative diseases (NDs) are a set of progressive, chronic, and incurable diseases characterized by the gradual loss of neurons, culminating in the decline of cognitive and/or motor functions. Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most common NDs and represent an enormous burden both in terms of human suffering and economic cost. The available therapies for AD and PD only provide symptomatic and palliative relief for a limited period and are unable to modify the diseases’ progression. Over the last decades, research efforts have been focused on developing new pharmacological treatments for these NDs. However, to date, no breakthrough treatment has been discovered. Hence, the development of disease-modifying drugs able to halt or reverse the progression of NDs remains an unmet clinical need. This review summarizes the major hallmarks of AD and PD and the drugs available for pharmacological treatment. It also sheds light on potential directions that can be pursued to develop of new, disease-modifying drugs to treat AD and PD, thereby decreasing the social and economic burdens linked to these NDs.

The objective of this study is to determine the relationship between body condition score (BCS) and body carcass fat (BCF) of ewes at two key stages of the production cycle. Data were collected from 88 Coopworth ewes including BCS measurements manually evaluated by the farm manager, BCF measurements determined by computerized tomography (CT) and body parameter measurements automatically determined by an image processing application. Artificial neural network (ANN) statistical analysis method was used to develop a relationship between the body parameters and the BCF with an r2=0.96 and RMSE=1.01 using all top and side variables.

The ultimate goal of our review is to draw clinician’s attention, especially of those who are managing pediatric patients with permanent, long-standing, ventriculoperitoneal or, less commonly, ventriculo-atrial shunts. This specific subgroup of patients may eventually suffer from severe, intractable headaches which may negatively impair their quality of daily living. In the absence of any other clinical condition that could be attributed as the cause of the headache, shunt overdrainage should not be overlooked but, on the contrary, should be seriously taken into consideration in the therapeutic armamentarium of such cases, that are difficult to be handled.

Reservoirs created by damming rivers significantly modify the environment and problems may arise: the storage of nutrients and organic matter in reservoirs often leads to decreased water quality due to eutrophication. Reservoirs built on lowland rivers are exposed to significant nutrient loads, often leading to eutrophication. In this article, the Sulejów Reservoir, in Central Poland, was the subject of the research, which focused on the use of Sentinel-2 remote sensing data to detect phytoplankton blooms, and on understanding the pattern of wind-driven surface currents using the CCHE2D model (a depth-averaged hydrodynamic model). The calculation of the total phosphorus load has shown that the reservoir mainly acts as nutrient storage. Still, during the low flow and intensive phytoplankton bloom, it can be a source of nutrients. The distribution of phytoplankton bloom on the Sulejów Reservoir was documented using eight Sentinel-2 satellite images from the vegetation season of 2020 and the Normalized Difference Chlorophyll Index (NDCl). Coupling remote sensing data and numerical modelling helps to interpret the hydrodynamic model results and understand nutrients and sediment dynamics within the reservoir. Hydrodynamic modelling combined with remote sensing data can be a valuable tool for selecting appropriate locations for water quality measurements at monitoring stations.

A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral feature and color feature at the same time, and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu ’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu ’er tea were obtained by this system and used for pixel classification. The experimental results show that the accuracy of Support Vector Machine (SVM) model based on combined features is 93%, which is 7% higher than that based on only spectral features. By applying median filtering algorithm and contour detection algorithm to the label matrix extracted from pixel-classified images, 8 impurities except hair were detected successfully. Moreover, taking advantage of the high resolution of visible light camera, small impurities can be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it is proved that the system can obtain multiple images to allow a more detailed and comprehensive understanding of the detected items, and has excellent ability to detect small impurities.

The Amazon region has the most incredible diversity of freshwater stingrays; however, there are still places where information about this group is scarce. The present work aims to record the occurrence of freshwater stingrays in the Uatumã river basin, Amazonas, Brazil. For this, collections were carried out in three municipalities bathed by the Uatumã River, Presidente Figueiredo, Itapiranga and São Sebastião do Uatumã. The rays were collected through fishing using a hand net and, after capture, were identified through the pattern of the dorsal and ventral color of the disc, the absence or presence of the labial groove and the organization of the row of spines on the tail, too were also measured, weighed and sexed. In total, 69 specimens of Potamotrygonines were captured, 59 of which were from the genus Potamotrygon and ten individuals from the genus Paratrygon. The present study provides preliminary information on the characteristics of freshwater stingray species that may occur in the Uatumã River. Furthermore, new research is necessary to improve the identification of specimens to determine the diversity and elucidate aspects related to the biology of elasmobranchs in the Uatumã River Basin, Brazil.

Given the financial demands of attending college, the transition to new living situations, abrupt changes in social support, and overall lifestyle adjustments, college students are at an increased risk of food insecurity (FI) compared to the general population. Collegiate athletes experience an even greater risk of FI as a result of greater time commitments and energy demands associated with their sports. This heightened vulnerability poses a tremendous threat to student-athletes’ academic and athletic achievements. This study aims to address the prevalence and primary determinants of FI among collegiate athletes while providing potential solutions to navigate and alleviate the effects of diminished food security among this demographic. To address these aims, a total of 18 articles were selected from both peer-reviewed and gray literature. The U.S. Household Food Security Survey Module (US-HFSSM) survey tools were predominantly utilized across universities throughout the United States to gather data on FI. Student-athletes reported experiencing FI across various regions of the United States, including universities in northeastern states (n=5), the southwest region (n=3), the southeast region (n=3), the northwest (n=1), and the Midwest (n=1). Overall, FI prevalence rates ranged from 9.9% to 65% and the most significant contributors included limited financial resources, time management, meal plans, and housing location/amenities. These findings highlight a need for screening, education, and intervention to address FI among collegiate athletes.

Tanning, crucial for leather production, relies heavily on chromium yet poses risks due to chromium's oxidative conversion, leading to significant wastewater and solid waste generation. Physico-chemical methods are typically used for heavy metal removal, but they have drawbacks, prompting interest in eco-friendly biological remediation techniques like biosorption, bioaccumulation, and biotransformation. The EU Directive (2018/850) mandates alternatives to landfilling or incineration for industrial textile waste management, highlighting the importance of environmentally conscious practices for leather products' end-of-life management, with com-posting being the most researched and viable option. This study aimed to isolate microorganisms from tannery wastewater and identify those responsible for different types of tanned leather biodegradation. Using a leather biodegradation assay (ISO 20136) with tannery and municipal wastewaters as inoculum, bacterial shifts during leather biodegradation were observed. Over 10,000 bacterial species were identified in all analyzed samples, with 8 bacterial strains isolated from tannery wastewater. Identification of bacterial genera like Acinetobacter, Brevundimo-nas, and Mycolicibacterium provides insights into potential microbial candidates for enhancing leather biodegradability, wastewater treatment, and heavy metal bioremediation in industrial applications.

Despite significant advances in tumor biology and clinical therapeutics, metastasis remains the primary cause of cancer-related deaths. While RNA-seq technology has been used extensively to study metastatic cancer characteristics, challenges persist in acquiring adequate transcriptomic data. To overcome this challenge, we propose MetGen, a generative contrastive learning based on deep learning model. MetGen generates synthetic metastatic cancer expression profiles using primary cancer and normal tissue expression data. Our results demonstrate that MetGen generates comparable samples to actual metastatic cancer samples, and we discuss the learning mechanism of MetGen. Additionally, we demonstrate MetGen's interpretability using metastatic prostate cancer and metastatic breast cancer. MetGen has learned highly relevant signatures in cancer, tissue, and tumor microenvironment, such as immune response and the metastasis process, which potentially fosters a more comprehensive understanding of metastatic cancer biology. The development of MetGen represents a significant step toward the study of metastatic cancer biology by providing a generative model that identifies candidate therapeutic targets for the treatment of metastatic cancer.

Relative to several model bacteria, the ethanologenic bacterium Zymomonas mobilis is shown here to have an elevated resistance to exogenous antimicrobial peptides (AMPs) – both, with regard to peptide bulk concentration in the medium, and the numbers of peptide molecules per cell. By monitoring integration of the AMPs in the bacterial cell membrane and observing the resulting effect on membrane energy-coupling, it is concluded that the membranotropic effects of the tested AMPs in Z. mobilis and in Escherichia coli are comparable. The advantage of Z. mobilis over E. coli apparently results from its uncoupled mode of energy metabolism that, in contrast to E. coli, does not rely on oxidative phosphorylation, and hence, is less vulnerable to disruption of its energy-coupling membrane by AMPs. It is concluded that the high resistance to antimicrobial peptides (AMPs) observed in Z. mobilis not only proves crucial for its survival in its natural environment but also offers a promising platform for AMP production and sheds light on potential strategies for novel resistance development in clinical settings.

Poplars (Populus spp.) are of significant ecological and economic importance. Long-term breeding efforts were aimed mainly at obtaining fast-growing and productive plants and less considered resistance to pests. This study aimed to identify the patterns of susceptibility to Saperda carcharias (Linnaeus, 1758) (Coleoptera: Cerambycidae) among Populus hybrids and pure species, considering crossing combinations and some other traits. In our research, 35 clones of poplar species and hybrids of the Ukrainian and foreign selection from the Aigeiros, Tacamahaca, and Leucoides sections were tested in 2021 and 2023 in the plantation created in 2014 in Eastern Ukraine. S. carcharias preferred infesting clones with earlier foliage development, larger diameters, and height increments. Specifically, hybrids with American maternal lineage showed the highest susceptibility, whereas those with European maternal lineage or Asian origin displayed lower infestation levels or resistance, respectively. This variability suggests a complex relationship between genetic background and pest resistance, underlining the need for a deeper understanding of the factors influencing susceptibility to S. carcharias. Selecting native species clones or creating mixed clone plantations could enhance the resilience of poplar plantations to pest threats.

Cultural routes are a composite set of heritage, referring to any ancient routes of human communication. As one of the key products of cultural tourism, they providing visitors with a rich cultural experience across regions. We systematically review reports and studies related to the tourism development of 38 cultural routes cases around the world, with a special focus on their distribution, typology, planning patterns and tools for cultural tourism. We summarized eight tools, there are some differences in how often these eight tools are used by the different types of routes and different planning patterns for routes tourism. The study also made an evaluation system based on the conservation principles of cultural routes to find out how different tools of tourism affect the conservation and development of the historical region. It was found that although many efforts have been made by tourism decision-makers to protect and develop cultural routes there are still many problems and challenges in the process of tourism development along cultural routes. We close the paper by making a few recommendations for decision makers and researchers concerning future routes’ tourism planning and study.

This work presents the mathematical framework of the Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “2nd-CASAM-L”), which enables the most efficient computation of exactly obtained mathematical expressions of first- and second-order sensitivities of a generic system response with respect to functions (“features”) of model parameters. Subsequently, the first- and second-order sensitivities with respect to the model’s uncertain parameters, boundaries, and internal interfaces are obtained analytically and exactly, without needing large-scale computations. Within the 2nd-FASAM-L methodology, the number of large-scale computations is proportional to the number of model features (defined as functions of model parameters), as opposed to being proportional to the number of model parameters, which are considerably more than the number of features, being incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities (of any order) with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces. The application of the 2nd-CASAM-L methodology is illustrated using a simplified energy-dependent neutron transport model of fundamental significance in nuclear reactor physics.

The Yixian Formation (Lower Cretaceous) of China is famous worldwide for its fossils of early angio-sperms, but there is only one record of flower bud hitherto. A previous fossil record of flower bud just showed the appearance and lacked any internal details. Such a lacuna in knowledge of flower buds hin-ders our understanding of the evolution of flowers. Our new specimen was collected from an outcrop of the Yixian Formation (the Barremian-Aptian, Lower Cretaceous) near Dawangzhangzi, Lingyuan, Liao-ning, China. The specimen was observed and photographed using a Sony ILCE-7 digital camera and a Nikon SMZ1500 stereomicroscope, and details were observed using a TESCAN MAIA3 SEM. Our ob-servations document a fossil flower bud, Archaebuda cretaca sp. nov, from the Lower Cretaceous of China. The occurrence of two species of Archaebuda in the Yixian Formation not only confirms the ex-istence of the expected gynoecium (and possible androecium) in a flower bud, but also underscores the occurrence of typical flowers in the Early Cretaceous.

This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—Artificial Neural Network, Multiple Linear Regression, Support Vector Machine, and Regression Tree—are employed and compared for performance. Evaluation metrics such as Mean Absolute Deviation, Root Mean Square Error, Coefficient of Correlation, and Mean Absolute Percentage Error are used. After preprocessing 1030 samples, the dataset is split into 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process.

The physical roots, interpretation, controversies and precise meaning of the Landauer Principle are surveyed. Landauer's principle is a physical principle pertaining to the lower theoretical limit of energy consumption of computation. It states that an irreversible change in information stored in a computer, such as merging two computational paths, dissipates a minimum amount of heat kBTln2 per a bit of information to its surrounding. The Landauer Principle is discussed in the context of fundamental physical limiting principles, such as the Abbe diffraction limit, the Margolus-Levitin limit and the Bekenstein limit. Synthesis of the Landauer bound with the Abbe, Margolus-Levitin limit and Bekenstein limits yields the minimal time of computation, which scales as τmin~hkBT. Decrease in a temperature of a thermal bath will decrease the energy consumption of a single computation, but, in parallel, it will slow the computation. The Landauer principle bridges between John Archibald Wheeler “it from bit” paradigm and thermodynamics. Experimental verifications of the Landauer Principle are surveyed. Interrelation between thermodynamic and logical irreversibility is addressed. Generalization of the Landauer principle to the quantum and non-equilibrium systems is addressed. Landauer Principle represents the powerful heuristic principle bridging the physics, information theory and computer engineering.

Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into missing data problem formulation, assumption specification, and method development. In this paper, we highlight the gap particularly for observational data from healthcare facilities. We address this gap by identifying ten fundamental missingness scenarios arising during data measurement, recording, and pre-processing in observational health data, influenced by physicians, patients, healthcare facilities, and data scientists. We analyze the effect of scenarios on estimand formulation, missing data identification, estimation, and sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis (complete-case analysis, Missforest imputation, and inverse probability weighting estimation) for two estimands (variable mean estimation and classification accuracy). We advocate for our analysis approach as a reference for the analysis of observational health data. Furthermore, we posit that the proposed analysis framework is applicable to other medical domains, including medical wearable data analysis.

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and re-analysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, to capture the nuanced dynamics of Cyprus's atmospheric conditions. With a temporal resolution of 15 minutes and a spatial of 0.05°x0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models' accuracy but also highlights their proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), of -2 to -4% per decade have been found in Autumn, especially marked on the island’s Northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of -2 to -3% per decade that were found in Spring do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the Daylight Integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

This paper presents two control design strategies for voltage regulation in a single inductor dual output (SIDO) DC-DC Buck converter system. Based on a nominal multiple input multiple output (MIMO) plant model and performance requirements, both an LQR and a decoupled PI control laws are designed to control the power converter system, under parametric uncertainties such as voltage source variation, CPL power variation, and load resistance variation. Therefore, the SIDO converter can cascade operating, which may cause the undesired effects of voltage oscillation and reduce the system's stability margin. The control performance was assessed under both uncertainties resistive loading and power variation of a constant power load (CPL). A SIDO DC-DC Buck converter board has been developed for experimental tests. The experimental results show that the LQR strategy, compared to the PI strategy, presented a robust performance in the presence of parametric uncertainties in the resistive loads being, however, sensitive to uncertainties due to the presence of CPL loads.

Dysregulation of the Cyclin-dependent kinase 8 (CDK8) activity has been associated to many diseases including the colorectal and breast cancer. As usual in the CDK family, the activity of CDK8 is controlled by a regulatory protein called cyclin C (CycC). But while human CDK family members are generally activated in two steps that is, the binding of the cyclin to CDK and the phosphorylation of a residue in the CDK activation loop, CDK8 does not require the phosphorylation step to be active. Another peculiarity of CDK8 is its ability to be associated to CycC while adopted an inactive form. These specificities raise the question of the role of CycC in the complex CDK8-CycC, which appears to be more complex than for the other members of CDK family. Through MD simulations and binding free energy calculations, we investigated the effect of CycC on the structure and dynamics of CDK8 on the one hand, and the structural molecular basis of the protein-protein interaction between the two partners on the other hand. We found that CycC has a stabilizing effect on CDK8 and identified specific interaction hotspots within its interaction surface compared to other human CDK/Cyc pairs. Targeting these specific interaction hotspots could be a promising approach in terms of specificity, to effectively disrupt the interaction between CDK8. The simulation of the conformational transition from the inactive to the active form of CDK8 suggests that the residue Glu99 of the CycC may assume the role of the missing phosphorylation step in the activation mechanism of CDK8. In a more general view, these results point the importance of keeping the CycC in computational studies when studying the human CDK8 protein in both the active and the inactive form.

The causes of algal blooms in reservoirs are often complexly intertwined with chemical, physical, and biological factors such as the supply of nutrients. Observation of phytoplankton distribution with high spatiotemporal resolution is necessary to track the nutrient sources that cause algal blooms and to understand their behavior in response to wind and water temperature stratification. Observation from a UAV, which has excellent temporal and spatial resolution, is considered to be an effective method to obtain water quality information comprehensively. On the other hand, it is not only the growth of plankton that affects the color of the water surface but also turbidity. Furthermore, since the brightness value of passive sensors such as optical cameras changes depending on the amount of insolation, it is necessary to perform analysis after making corrections for this. In this study, we attempted to develop a method for estimating chlorophyll concentration from aerial images taken from UAVs using machine learning that takes into account brightness correction based on insolation and the spatial distribution of turbidity evaluated by satellite image analysis.

As part of the Fabaceae project of northeastern Mexico, today we present the compendium of legumes of the subfamilies Caesalpinioideae, Cercidoideae and Detarioideae from this region. As in our previous work on the Mimosoideae Clade, new nomenclatural changes and retypification of genera of the subfamily Caesalpinioideae are included. Also, based on new scientific information published, the taxonomic changes in new genera, recently segregated from the genus Caesalpinia (Conzattia, Coulteria, Denisophytum, Erythrostemon, Guilandina, Hoffmannseggia, and Pomaria) are included. Based on field work, collection of botanical samples over the past 37 years, and reviewing botanical materials in national and international herbaria, the diversity of legumes of the subfamilies Caesalpinioideae, Cercidoideae, and Detarioideae in northeastern Mexico has been recorded. The subfamily Caesalpinioideae includes two tribes, the tribe Caesalpinieae with 12 genera (Caesalpinia, Ceratonia, Coulteria, Delonix, Denisophytum, Erythrostemon, Gleditsia, Guilandina, Hoffmannseggia, Haematoxylum, Parkinsonia, and Pomaria), and 26 species and the tribe Cassieae, which includes three genera (Cassia, Chamaecrita, and Senna) and 28 species. The subfamily Cercidoideae include two genera (Bauhinia and Cercis), and eight species and the subfamily Detarioideae, includes only one genus and one species (Tamarindus indicus). The total flora of these three subfamilies comprize 18 genera and 63 species, it includes 56 native species and seven exotic ones: Bauhinia variegata, Cassia fistula, Ceratonia siliqua, Delonix regia, Erythrostemon gilliesii, Senna alata, and, Tamarindus indicus. The endemism of both subfamilies includes a total of 20 species and 10 infraspecific categories.

Recent research efforts in bone tissue engineering have been primarily directed towards manufacture-viable synthesis of biomaterials that can significantly enhance the biocompatibilities and osteogenic capabilities on the new biomaterials. This paper presents a straightforward, cost-effective, optimized, and well-controlled hydrothermal synthesis of Nb-doped potassium titanate nanofibers in high-purity. Characterization data revealed that the Nb-doping potassium titanate maintained the crystal structure, showing great promise for applications in bone tissue engineering.

To enhance pomegranate production on marginal gravelly lands, our study evaluated standardized planting techniques in an 8-year-old orchard. We employed trenching, wider pit excavation, pit digging, and auger digs with dimensions of 1 and 2 meters. Utilizing native soil from barren land, with or without spent wash, and mixing it with black soil up to 1 meter deep, we assessed growth parameters, leaf nutrients, and fruit yield. The trench and wider pit methods outperformed others, yielding greater above-ground biomass (>7.03 t tree-1), root biomass (2.5 t tree-1), and cross-sectional area (3.3 m² tree-1). Additionally, trench planting enhanced leaf phosphorus (0.28%) and potassium (1.8%) levels, fruit juice content (48.5%), total soluble solids (16.05°), and fruit yield (>9.3 t ha-1). The trench method also fostered longer roots at 90–150 cm radial distance and deeper roots at 40–60 cm depth. In summary, the trench and wider pit methods, combined with a soil mixture, are recommended for sustainable, high-quality fruit production in shallow gravelly terrains, thereby improving food security and the livelihoods of farmers in arid regions.

This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, Fourier Granger Causality, and Fourier Toda Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.