Hydrogen sulfide (H2S) is an environmental toxicant of health concern. The brain is a major target in acute H2S poisoning. This study was to test the hypothesis that acute and subchronic ambient H2S exposures alter the brain metabolome. Male 7-8 week-old C57BL/6J mice were exposed by whole-body inhalation to 1000 ppm H2S for 45 min and euthanized at 5 min or 72 h for acute exposure. For subchronic study, mice were exposed to 5 ppm H2S 2 h/day, 5 days/week for 5 weeks. The brainstem was removed for metabolomic analysis. Enrichment analysis showed the metabolomic profiles in acute and subchronic H2S exposures matched with those of cerebral spinal fluid from patients with seizures or Alzheimer’s disease. Acute H2S exposure decreased excitatory neurotransmitters, aspartate and glutamate, while the inhibitory neurotransmitter, serotonin, was increased, which may explain H2S-induced loss of consciousness. Branched-chain amino acids and glucose were increased by acute H2S exposure. Subchronic H2S exposure within OSHA guideline surprisingly decreased serotonin altering central nervous system. In subchronic H2S exposure, glucose was decreased while polyunsaturated fatty acids, inosine, and hypoxanthine were increased. Collectively, these results provide important mechanistic clues of acute and subchronic ambient H2S poisonings and show that H2S alters brainstem metabolome.

Background and Objectives: Wilson disease (WD) is a disorder of copper metabolism caused by genetic muta-tions in the ATP7B gene which lead to the accumulation of copper in the body. The aim of the present study is to investigate the severity of WD in patients in Romania. Materials and Methods: This study was conducted us-ing an online questionnaire consisting of 32 questions in a group of patients suffering from Wilson disease. Results: The study included 67 people aged 7 to 56 with WD. The most common symptoms of the participants were hepatic and neurological in 22 cases (32.8%). The most common neurological symptom in the assessed cases was muscle stiffness (49.5%), followed by tremor (45%), and speech disorders (40.5%). The Kay-ser-Fleischer ring was present in 50.7% of the participants. The most commonly used drug treatment in the participants was D-penicillamine (77.61%). Conclusions: The severity of WD is based on many aspects, the most important being early diagnosis and access to treatment throughout the year. The prognosis is favorable with early diagnosis and treatment, but it is crucial to diagnose individuals before they develop major symp-toms. Therefore, advances in WD screening can lead to an earlier diagnosis and better results.

For the better part of a century researchers across disciplines have sought to explain the crystallography of the elemental transition metals: hexagonal close packed, body centered cubic, and face centered cubic in a form similar to that used to rationalize the structure of organic molecules and inorganic complexes. Pauling himself tried with limited success to address the origins of transition metal stability. These early investigators were handicapped, however, by incomplete knowledge regarding the structure of metallic charge density. Here we exploit modern approaches to charge analysis to first comprehensively describe transition metal charge density. Then, we use topological partitioning and quantum mechanically rigorous treatments of kinetic energy to account for the structure of the density as arising from the interactions between metallic tetrahedra. We argue that the crystallography of the early transition metals results from charge transfer from the so called “octahedral” to “tetrahedral holes” while the face centered cubic structure of the late transition metals is a consequence of antibonding interactions that increase octahedral hole kinetic energy.

This study investigates proximate drivers of cropland and forest degradation in Kloto district (Togo, West Africa) as, way of, exploring integrated sustainable landscape approaches in respect to socio-economic and environmental needs and requirements. Net change analysis of major cash and food crops based on three time steps Landsat data (1985–2002, 2002–2017 and 1985–2017) and quantitative analysis from participatory survey data with farmers and landowners are used. Study underlines poor agricultural systems and cassava farming as major impediments to alarming forest losses between 1985–2017. Significant net loss in forests cover by 23.6% and surface areas under cultivation of cocoa agroforestry and maize by 12.99 and 10.1% from 1985 to 2017, due to, intensive cassava cropping (38.78%) and settlement expansions (7.84%). Meanwhile, loss in forest cover between 2017 and 2002 was marginal (8.36%) compared to the period 1985–2002 for which the loss was considerable (15.24%). Based on participatory surveys, majority of agricultural lands are threatened by erosion or physical deterioration (67.5%), land degradation or salt deposits and loss of micro/macro fauna and flora at 56.7%, declining in soil fertility (32.5%), soil water holding capacity (11.7%) and changes in soil texture (3.3%). Majority of farmers adhere to the adoption of the proposed climate smart practices with emphasis on cost effective drip irrigation systems (45.83%), soil mulching (35%) and adoption of drought resilient varieties (29.17%) to anticipate drought spells adverse. The study concludes that low adoption of improved soil conservation, integrated water management and harvesting systems and low productive and adaptive cultivars entail extreme degradation of croplands and crops productivity decline. Therefore, farmers are forced to clear more forests in search of stable and healthy soils for production and extraction of forest products to meet their food demands and improve their livelihoods conditions. Capacity building on integrated pathways of soil and land management practices are therefore needed to ensure sustainable and viable socio-ecological systems at local scale.

Paediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS) is a newly described condition. It has a spectrum of presentations related to hyperinflammation and cytokine storm. We report the first case of PIMS-TS in a child on established anti-Tumor Necrosis Factor-alpha (anti-TNF-α) therapy; a 10 year-old girl with ulcerative colitis treated with infliximab. The patient had 6-weeks of daily fever with mucocutaneous, gastrointestinal, renal and hematologic involvement. Biomarkers of hyperinflammation were present including: hyperferritinaemia (up to 691 µ/L; normal 15-80 µg/L), C-reactive protein (CRP) (>100mg/L for >10 days, normal 0-5 mg/L), erythrocyte sedimentation rate (ESR) consistently >100mm/hr (normal 0-15 mm/hr), raised white cell count with neutrophilia, elevated D-dimer and lactate dehydrogenase (LDH), anaemia and Mott cells on bone marrow analysis. Extensive investigations for alternative diagnoses for pyrexia of unknown origin (PUO) were negative. The condition was refractory to treatment with intravenous immunoglobulin (IVIG) but improved within 24hrs of high dose methylprednisolone. Infliximab treatment followed and the patient has remained well at follow up. Polymerase chain reaction (PCR) and serology for SARS-CoV-2 were negative. Current series report such negative findings in up to half of cases. The patient experienced a milder clinical phenotype without cardiac involvement, shock or organ failure. It is postulated that prior anti-TNF-α therapy attenuated the disease course. Infliximab therapy may interfere with serology testing and produce false negative results. This case supports the need for investigation into infliximab as primary therapy for PIMS-TS.

In this paper, we present sums and partial sums of Horadam sequences via generating functions which extends a recent result of Prodinger.

In this paper, closed forms of the sum formulas $\sum_{k=0}^{n}x^{k}W_{k}^{3}$ and $\sum_{k=1}^{n}x^{k}W_{-k}^{3}$ for the cubes of generalized Fibonacci numbers are presented. As special cases, we give sum formulas of Fibonacci, Lucas, Pell, Pell-Lucas, Jacobsthal, Jacobsthal-Lucas numbers.

Background: Elevated respiratory rates (RR) have been described in several disease states, such as pneumonia, asthma and bronchiolitis. Despite this, there are few studies defining the range of RR found in healthy children. Available age percentiles for RR have been generated using methods that have the potential themselves to alter the rate. Aim: The aim of this study was to develop accurate percentile charts of RR specific for age in healthy children for potential use in pre-hospital care (first-aid), clinical medicine and for reference in respiratory research. Method: A newly developed optical respiratory sensor was used to measure RR which placed no restrictions on respiratory excursion. This technique enabled recording in awake children without the artefact of the observer’s presence on the subject’s RR. A cross-sectional sample of healthy children was obtained from maternity wards, childcare centres and schools in Brisbane, Queensland, Australia. Results: RR were observed in 560 awake and 103 sleeping children of which data from 343 awake and 94 sleeping children was used to create reference ranges for healthy children from newborn to 13 years of age. The recorded rates were significantly higher when children were awake compared to asleep. During quiet sleep, RR decreased from 40 (interquartile range 7) bpm at one month to 20 (interquartile range 3) in children aged 3 years. In awake children, RR ranged from a median of 40 bpm (interquartile range 18) at 1 year to 12 bpm (interquartile range 11) at 13 years respectively. Conclusion: The optical sensor was found to be an appropriate respiratory transducer, capable of measuring RR and reducing artefact by the subjective responses of alert children. The centile charts will be helpful as an aid to detecting abnormal RR in children and will contribute to further systematic reviews related to this vital sign.

The excessive intake of sodium (Na) and insufficient intake of potassium (K) are major concerns to prevent hypertension. Using low-Na/K seasonings (reducing 25% of the NaCl and adding K salt) may improve the dietary Na/K ratio and help prevent hypertension. To devise an intervention study using low-Na/K seasonings at a company cafeteria, we calculated the Na and K contents of the meals served at the cafeteria and estimated changes in the intakes when suitable low-Na/K seasonings were used. We also considered using milk as a good source of K. We used an ingredient list of a company cafeteria and calculated Na and K contents in each dish. The average amounts of NaCl and K per use were 5.04 g and 718 mg, respectively. Seasonings contributed 70.9% of the NaCl. With the use of low-Na/K seasonings, an estimated reduction in NaCl of 0.8 g/day and an estimated increase in K of 308 mg/day was achieved. An additional serving (200 mL) of milk, NaCl was reduced by 0.57 g/day and K was increased by 610 mg/day, with an overall decrease in the dietary Na/K ratio from 3.20 to 2.40. The use of low-Na/K seasonings and dairy may improve the dietary Na/K ratio among cafeteria users and help prevent hypertension.

Drawing from literature and Geert Hofstede’s cultural dimensions, this study delves into the impact of cultural factors on family structures, roles, and relationships. Ghana and Latvia have different cultural and social orientations that influence family dynamics. Through a comparative lens, this paper will contribute to understanding how a country’s culture influences family dynamics, highlighting the influence of Power Dynamics of parenting style and Individualism vs. Collectivism on interaction levels within the family.

To understand the health impacts of “hikikomori” lifestyle and to establish its first comprehensive health profile, a cross-sectional study was designed to measure how well the cases of hikikomori youths of Hong Kong were living, in terms of social, mental and physical aspects. This study involved 104 eligible participants at age 19.02 year-old who had completed the set of questionnaires and a series of anthropometric and physical health measurements. Despite SF36 score of 84.0 indicated good physical functioning in general, participants were lived sedentarily with high incidence of hypertension at 15.4% and prehypertension at 31.7%. Occurrence of hypertension in cases living as hikikomori >6 months was 3-times higher than those newly onset cases. The blood pressure levels were correlated with age and all obesity index parameters measured including waist circumference and body mass index. Half of the hypertensive cases involved the elevation of systolic blood pressure, which suggested higher odds of cardiovascular complications. Participants were mentally stable living with moderate levels of perceived stress and state anxiety, but borderline clinical depression. In conclusion, the hikikomori lifestyle could be a risk behavior that may harm the younger generation physically by promoting obesity and hypertension and probably other chronic illnesses.

Weather conditions appear to be undergoing significant deviations from the long-term average, marked by pronounced extremes of heat, prolonged droughts, and heightened rainfall occurring with greater frequency worldwide. Consequently, new patterns of extreme weather are emerging, like the unusual and unorganized tropical cyclone called "Yaku", that influenced the extreme amount of rain that between March 6th to 10th, 2023 hits more than 1000 districts in the northwestern of Peru. One of the district more affected was Íllimo, in the Lambayeque province, due to the river that pass through the city, so called “La Leche”, after the continuous and intensive rain overflow devastating his surrounding areas and causing victims and damage. This emergency provided and excellent opportunity to apply, on the aforementioned areas, the “change detection” technique, that allows identifying areas of change between data obtained before and after of emergency events. In this research, optical data (Sentinel-2, Landsat, MODIS Terra and Aqua, and PeruSAT-1) and synthetic aperture radar (SAR) data (Sentinel-1 and COSMO SkyMed) were analyzed. A benchmark could also be carried out between open-source and commercial spatial analysis tools. The results indicated that in an extreme rain event the optical data are obscured by clouds and do not allow their use, while SAR data overcome clouds, and can be used for this research. The benchmark indicated that using the Jaccard index score, the Sentinel-1A set showed a 33% of correlation, while COSMO SkyMed demonstrated a 38% of match between the maps. The results obtained could be used to sensitize the population and develop a better management of future emergency events (floods) in different areas of Peru.

K-band radar waves have high penetration and low attenuation coefficients. However, the wavelength of this radar wave is relatively short; thus, designing and preparing both broadband and wide-angle radar wave absorbers in this band presents considerable challenges. In this study, a resin-based K-band radar wave absorber with a biomimetic lotus leaf structure was designed and formed by UV curing. Here, microscale lotus leaf papillae and antireflection structures were prepared using a DLP 3D printer, and the contact angle between the material and water droplets was increased from 56° to 130°. In addition, the influence of the geometric parameters of the lotus leaf antireflection structure on the electromagnetic absorption performance and mechanical strength was investigated. After simulation optimization, the maximum electromagnetic loss of the lotus leaf structure 3D printed sample was −32.3 dB, and the electromagnetic loss was below −10 dB in the 20.8–26.5 GHz frequency range. When the radar incidence angle was 60°, the maximum electromagnetic loss was still less than −10 dB. The designed lotus leaf structure has a higher mechanical energy absorption per unit volume (337.22 KJ/m3) and per unit mass (0.55 KJ/Kg) than commonly used honeycomb lightweight structures during the elastic deformation stage, and we expect that the designed structure can be used as an effective lightweight material for K-band radar stealth.

Since its introduction, the Triangulation number has been the most successful and ubiquitous scheme for classifying spherical viruses. However, despite its many successes, it fails to describe the relative angular orientations of proteins, as well as their radial mass distribution within the capsid. It also fails to provide any insight into critical sites of stability, modifications or possible mutations. We show how classifying spherical viruses using icosahedral point arrays, introduced by Keef and Twarock, unveils new geometric rules and constraints for understanding virus stability and key locations for exterior and interior modifications. We present a modified fitness measure which classifies viruses in an unambiguous and rigorous manner, irrespective of local surface chemistry, steric hinderance, solvent accessibility or triangulation number. We then utilize these point arrays to explain the immutable surface loops of bacteriophage MS2, the relative reactivity of surface lysines in CPMV and the non-quasiequivalent flexibility of the HBV dimers. We explain how using sister and double point arrays can function as predictive tools for site directed modifications in other systems. This success builds on our previous work showing that viruses place their protruding features along the great circles of the asymmetric unit, demonstrating that viruses indeed adhere to these geometric constraints.

Plants have developed unique mechanisms to cope with the harsh environmental conditions to compensate for their lack of mobility. A key part of their coping mechanisms is the synthesis of secondary metabolites. In addition to their role in plants’ defense against pathogens, they also possess therapeutic properties against diseases, and their use by humans predates written history. Viruses are a unique class of submicroscopic agents, incapable of independent existence outside a living host. Pathogenic viruses continue to pose a significant threat on global health, leading to unprecedented fatalities on a yearly basis. The use of medicinal plants as a natural source for an-tiviral agents has been widely reported in literature in the past decades. Metabolomics is a powerful research tool for the identification of plant metabolites with antiviral potentials and can be used to isolate compounds with antiviral activities in plants and study the biosynthetic pathways involved in viral disease progression. This review discusses the use of medicinal plants as antiviral agents, with a special focus on the metabolomics evidence supporting their efficacy. Additionally, suggestions are made for the optimization of various metabolomics methods for characterizing the bio-active compounds in plants and subsequent understanding of the mechanism of their operation.

Equilibrium climate sensitivity (ECS) is the change in global mean temperature expected to result from doubling atmospheric CO2 concentration from pre-industrial levels. Extensive research during the past 40 years has not reduced the uncertainty associated with ECS. Sherwood et al. [1] applied Bayesian statistics to evidence from climate-process physics, historical observations and earlier proxies to reduce the range of ECS from 1.5 – 4.5 K to 2.6 – 4.1 K. This paper examines their methods and many of the assumptions they made. It also evaluates two additional periods in the Holocene to show that factors other than CO2 drove recent climate change. It identifies potential systematic errors resulting from adding non-equilibrium short-term adjustments to the radiative forcing of greenhouse gases and from underestimating the effects of solar irradiance, ocean currents and aerosols. These factors have resulted in estimates of the forcing by CO2 that far exceed the apparent effects in paleoclimate data.

Background: SARS-CoV-2 continues to impact human health globally, with airborne transmission being a significant mode for transmission. In addition to tools like vaccination and testing, counter-measures that reduce viral spread in indoor settings are critical. This study aims to assess the efficacy of UV-C light, utilizing the Violett sterilization device, as a countermeasure against airborne transmission of SARS-CoV-2 in the highly susceptible Golden Syrian hamster model. Methods: Two cohorts of naïve hamsters were subjected to airborne transmission from experimentally infected hamsters; one cohort was exposed to air treated with UV-C sterilization, while the other cohort was exposed to un-treated air. Results: Treatment of air with UV-C light prevented the airborne transmission of SARS-CoV-2 from experimentally exposed hamster to naïve hamsters. Notably, this protection was sustained over a muti-day exposure period during peak viral shedding from hamsters. Conclusion: These findings demonstrate the efficacy of the UV-C light to mitigate airborne SARS-CoV-2 transmission. As variants continue to emerge, UV-C light holds promise as a tool to reduce infections in diverse indoor settings, ranging from healthcare facilities to households. This study reinforces the urgency of implementing innovative methods to reduce airborne disease transmission and safeguard public health against emerging biological threats.

In recent years, news media has been greatly disrupted by the potential of technologically driven approaches in the creation, production, and distribution of news products and services. Artificial intelligence (AI) has emerged from the realm of science fiction and has become a very real tool that can aid society in addressing many issues, including the challenges faced by the news industry. The ubiquity of computing has become apparent and has demonstrated the different approaches that can be achieved using AI. We analyzed the news industry’s AI adoption based on the seven subfields of AI: (i) machine learning; (ii) computer vision (CV); (iii) speech recognition; (iv) natural language processing (NLP); (v) planning, scheduling, and optimization; (vi) expert systems; and (vii) robotics. Our findings suggest that three subfields are being developed more in the news media: machine learning, computer vision, as well as planning, scheduling, and optimization. Other areas have not been fully deployed in the journalistic field. Most AI news projects rely on funds from tech companies such as Google. This limits AI’s potential to a small number of players in the news industry. We make conclusions by providing examples of how these subfields are being developed in journalism and present an agenda for future research.

Non-Vitamin K Oral Anticoagulants (NOACs) have revolutionized anticoagulant therapy, offering improved safety and efficacy over traditional agents like warfarin. This review comprehensively examines the dual roles of NOACs—Apixaban, Rivaroxaban, Edoxaban, and Dabigatran—not only as anticoagulants but also as modulators of inflammation via Protease-Activated Receptor (PAR) signaling. We highlight the unique pharmacotherapeutic properties of each NOAC, supported by key clinical trials demonstrating their effectiveness in preventing thromboembolic events. Beyond their established anticoagulant roles, emerging research suggests that NOACs influence inflammation through PAR signaling pathways, implicating factors such as FXa and Thrombin in the modulation of inflammatory responses. This review synthesizes current evidence on the anti-inflammatory potential of NOACs, exploring their impact on inflammatory markers and conditions like atherosclerosis and diabetes. By delineating the mechanisms by which NOACs mediate anti-inflammatory effects, this work aims to expand their therapeutic utility, offering new perspectives for managing inflammatory diseases. Our findings underscore the broader clinical implications of NOACs, advocating for their consideration in therapeutic strategies aimed at addressing inflammation-related pathologies. This comprehensive synthesis not only enhances understanding of NOACs' multifaceted roles but also paves the way for future research and clinical applications in inflammation and cardiovascular health

Periodontitis is a chronic inflammatory disease affecting the tissues that surround and support the teeth. In the U. S., approximately 65 million people are affected by this condition. Its occurrence is also associated with many important systemic diseases such as cardiovascular disease, rheumatoid arthritis, and Alzheimer’s disease. Among the most important etiologies of periodontitis is Porphyromonas gingivalis, a keystone bacterial pathogen. Keystone pathogens can orchestrate inflammatory disease by remodeling a normally benign microbiota causing imbalance between normal and pathogenic microbiota (dysbiosis). The important characteristics of P. gingivalis causing dysbiosis are its virulence factors that cause effective subversion of host defenses to its advantage [1], allowing other pathogens to grow. However, the mechanisms involving these processes are poorly understood. However, various microbial strategies target host sialoglycoproteins for immune dysregulation. In addition, the enzymes that break down sialoglycoproteins/sialoglycans are the “sialoglycoproteases”, resulting in exposed terminal sialic acid. This process could lead to pathogen-toll like receptor (TLR) interactions mediated through sialic acid receptor–ligand mechanisms. By assessing the function of P. gingivalis sialoglycoproteases, could pave the way to designing carbohydrate analogues and sialic acid mimetics to serve as drug targets.

In the paper, the authors generalize the notion "k-Mittag-Leffler function'', establish some integral transforms of the generalized k-Mittag-Leffler function, and derive several special and known conclusions in terms of the generalized Wright function and the generalized k-Wright function.

This paper will explore analogs of groups formed by the transformations of two ropes as presented by Minh-Tam Quang Trinh.1 The two operations acting on these ropes are T and S, defined as twists and clockwise turns, respectively. These actions intertwine the ropes, creating tangles. T and S also have the ability to untangle the rope when used in certain combinations. In the two-rope case, these actions expressed as a group presentation were shown to be isomorphic to SL₂(Z). We explored the analog of these opera- tions, T and S, and conjectured the corresponding group to which they are isomorphic. Partial proofs for this conjecture are included and future work would include proving our conjecture as well as generalizing results to k ropes.

In this work, we introduce the TriFusion Network, an innovative deep learning framework designed for the simultaneous analysis of auditory, visual, and textual data to accurately assess emotional states. The architecture of the TriFusion Network is uniquely structured, featuring both independent processing pathways for each modality and integrated layers that harness the combined strengths of these modalities to enhance emotion recognition capabilities. Our approach addresses the complexities inherent in multimodal data integration, with a focus on optimizing the interplay between modality-specific features and their joint representation. Extensive experimental evaluations on the challenging AVEC Sentiment Analysis in the Wild dataset highlight the TriFusion Network's robust performance. It significantly outperforms traditional models that rely on simple feature-level concatenation or complex score-level fusion techniques. Notably, the TriFusion Network achieves Concordance Correlation Coefficients (CCC) of 0.606, 0.534, and 0.170 for the arousal, valence, and liking dimensions respectively, demonstrating substantial improvements over existing methods. These results not only confirm the effectiveness of the TriFusion Network in capturing and interpreting complex emotional cues but also underscore its potential as a versatile tool in real-world applications where accurate emotion recognition is critical.

The digital economy, driven by information and communication technologies (ICT), has profoundly transformed in recent decades. The digitalization of society has given rise to an economic environment in which information, connectivity, and innovation play fundamental roles. In this context, a technology that has emerged as a fundamental pillar of the digital economy is the chain of blocks, commonly known as blockchain. Blockchain is a technology that has revolutionized the way online data and transactions are managed and shared. Through its ability to create secure, transparent, and decentralized ledgers, blockchain has paved the way for the digital economy, facilitating trust in digital transactions and enabling various applications ranging from cryptocurrencies to supply chain management and intellectual property. This study will delve into blockchain and its influence on the digital economy. It will explore how this technology has reshaped how companies interact, how consumers access services, and how new business models are developed in a constantly evolving digital environment. Additionally, the challenges and opportunities that blockchain presents in the context of the digital economy will be analyzed, and how it is helping to shape the future of business and society in general. As the exploration of blockchain and its impact on the digital economy progresses, it becomes evident how these two forces converge, generating a promising digital landscape full of significant opportunities and transformations. This phenomenon is consistently supported by a growing body of research and analysis, which underlines the growing influence of blockchain on the global economy (Smith, 2020; Johnson, 2019). The dynamic interplay between these two spheres, blockchain and the digital economy, constantly evolves and offers an exciting glimpse into the future regarding innovation and disruption across various sectors (Jones, 2021; Brown, 2018). As a result, significant opportunities are looming for those seeking to understand and capitalize on these emerging trends (García, 2022). Throughout this study, the current trends and most intriguing perspectives that shape this landscape will be broken down, offering a deeper insight into how blockchain and the digital economy are shaping an extraordinary digital future

Background: Extremametly lean athletes could have difficulty for adaptive eating (intuitively or consciously) during ad libitum refeed. Aims: we verificate if there is a relation between adaptive eating and coping strategies with energy intake in a model of refeed proposed for bodybuilders. Methods: Fourteen male bodybuilders (29.6±3.1yrs; 85.6±6.8kg, ≥6 competitions) completed the 4-week each consisting of a 5 days of energy restriction following 2 days of refeed. Dietary assessment, body composition (ultrasound), Recovery stress questionnaire (REST-Q) and BRUMS (Brunel mood scale) were accessed pre and post; Coping Function Questionnaire (CFQ), Mindful Eating Scale version 2 (MES 2) and the Intuitive Eating Scale-2 (IES-2) was used at 4th week. Results: The refeed day resulted in a daily caloric increment of ~44% compared to the average energy intake on the energy restriction days, culminating in a weekly calorie deficit of ~27% and a drop in body mass of 3.1±1.4% with relation to initial values. The most reduced body fat and preserved or gained lean mass. The energy consumption during refeed maintained an inverse relationship with the perception of satiety (r=-0.9; p<0.01), total scores of IES 2 (r=-0.82; p<0.01), as well as with the confidence in hunger and satiety cues (r=-0.62; p=0.02) and congruence in food-body choice (r=-0.56; p=0.04) dimensions. Coping based in emotions maintained a relationship with the total scores of IES 2 (r=0.54; p<0.05) and an inverse relationship with energy intake during refeed (r=-0.42; p<0.05). Conclusion: Our results suggest that a higher perception of internal hunger and satiety signals and higher scores in intuitive eating may contribute to energy intake adequate, even when high scores of coping based in emotions are present.

Drainage systems are usually dimensioned for design storms based on intensity-duration-frequency (IDF) curves of extreme precipitation. For each location, different IDF curves are established based on local hydrological conditions. Recent research shows that these curves also vary with time, and should be updated with recent data. The purpose of this study is to evaluate IDF curves obtained from precipitation simulations from the Eta RCM, comparing them with IDF curves obtained from data of a rainfall station. Climate models can be a useful tool for assessing the impacts of climate changes on drainage systems, referring precipitation forecasts. In this study, the Eta RCM was forced by two global climate models: HadGEM2-ES and MIROC5. The bias of the precipitation data, generated by RCM models, was corrected using a Gamma distribution. The Juqueriquerê River Basin, in the cities of Caraguatatuba and São Sebastião, São Paulo State, Brazil, was chosen as a case study. The results show a good correlation between the IDF curves of simulated and observed rainfall for the control period (1960-2005), indicating the strong possibility of using the Eta RCM precipitation forecasts for 2007 - 2099 to establish future IDFs thereby, taking into account climate changes in urban drainage design.

The KcsA is a prokaryotic potassium-oriented channel, which is sourced from the Streptomyces lividans soil bacteria. From extensive studies that have been carried over the KcsA potassium channel, it has been proved that various factors enable the gating and shuttling of the potassium ions into and out of the cells. Some of the factors include the different concentration of protons in the inside and outside membrane. The other factor is the existence of the selectivity filter equipped in the exit of channel pore, which enables the movement of the K⁺ ions. Not only are potassium ions present in the channel but also sodium ions. Potassium and sodium ions are the ions that are essential in the conductivity of a cell because of their charge. An intuitive idea about why K⁺ ions are at least 10,000 times more permeant than Na⁺ ions is presented in various entities. The hypothesis is that the selectivity ratio is probably related to the successive entry of Na⁺ ions, based on the premise that the ratio seems to be relevant to unpredictable quantities caused by Brownian corrosion of ions.

Apex predators were extirpated from the UK hundreds of years ago. Practitioners have recently advocated for reintroductions of predators in the UK given their role as keystone species. For a reintroduction to take place, a survey of public desirability needs to be conducted. We aimed to understand the perspectives of people across the United Kingdom on the reintroduction of Eurasian lynxes, grey wolves, and brown bears. We collected data via questionnaires from 78 key stakeholders, such as farmers, general public, nature conservationists, and those in environmental sectors, with these areas being seen as the most affected by these reintroductions. It was organizations which front these stakeholder groups which mainly distributed the survey, as well as social media groups. We ran a path analysis, which highlighted that there was a negative correlation between respondents viewing the species as a danger towards livestock and humans, and the respondent seeing it being good to have that species in their region. Overall, participants who stated that rewilding their region was a priority also stated that it would be good to have Eurasian lynx in their region. That said, 70% of farmers strongly disagreed with the reintroduction of Eurasian lynx. These findings showed that much of the United Kingdom would welcome the reintroduction of Eurasian lynx, but both farmers and the public would fear the loss of livestock that may come along with these species. Support can be gained through education and knowledge, a necessary step needed especially targeting farmers before any reintroduction plans can be made.

The travel-goods industry is an essential part of the larger travel and tourism sector, but it creates significant environmental impacts due to resource and energy consumption. This study investigates the feasibility and sustainability potential of servitisation concepts within the travel-goods industry, and the Product-Service System (PSS) models, in particular, to identify steps towards a more sus-tainable travel industry in the future. It explores the sustainability-related drivers within the luggage industry and identifies barriers to the adoption of servitisation models, and opportunities for value creation for both consumers and commercial organisations. Business models are mapped into a ty-pology to highlight different pathways to PSS adoption, underpinned with empirical data collected via a consumer sentiment survey and semi-structured interviews with industry experts. Even though the analysis revealed shifting consumer attitudes towards servitisation concepts within the travel-goods market, with a significant level of interest emerging for specific PSS models, at present, the widespread adoption of PSS is hindered. This is due to the fragmented nature of global supply chains and entrenched ownership values. Addressing supply chain issues regarding end-of-life systems to sustainably manage products beyond functional obsolescence is critical. In parallel, product-oriented PSS models are more likely to increase, driven by a burgeoning resale market and supported by digi-tal technologies, which in turn can lead to greater prospects for use-oriented PSS adoption and even-tually, reduced environmental impacts.

Data from environmental satellites are underutilized in many branches of operational oceanography. Several challenges exist for potential users of satellite data that may impede or preclude employing satellite products in their work. Users outside of the satellite community often encounter difficulty in discovering the types of satellite measurements that are available, and determining which satellite products are best for operational activities. In addition, the large choice of satellite data providers, each with their own data access protocols and formats, can make data access confusing and inefficient. NOAA’s CoastWatch Program is designed to be a Value Added Provider, whose mission is to help make satellite data easier for users to access. With this objective the West Coast Node of CoastWatch developed the NOAA Ocean Satellite Course, which introduces scientists and resource managers to ocean satellite products, and provides them tools to facilitate data access when using ArcGIS or R software. These tools leverage upon the data services provided by ERDDAP, a data distribution system designed to make data access easier for both humans and machines. The course has been offered annually since 2006, and over 350 participants have taken it. Results of post-course surveys are analyzed to measure course effectiveness. Lessons learned from conducting these courses are summarized.

This report presents a framework for analysing the risk of alien taxa under South Africa's National Environmental Management: Biodiversity Act of 2004, and the Alien and Invasive Species Regulations of 2014. While the report was initially designed to meet a specific South Africa need, the risk analysis processes developed can, we believe, be transferred to any specified geographic region. In outlining a series of questions related to a taxon’s likelihood of invasion and the consequences thereof, i.e. the potential impacts, the report provides a structure for collating data relevant to the process of listing taxa as well as a process for developing recommendations that is both mathematically sound, transparent, and that explicitly takes uncertainty into account. The framework is based on collating information according to international standards in biological invasions (specifically the IUCN Environmental Impact Classification of Alien Taxa Scheme, the CBD's scheme for classifying invasion pathways, and the Unified Framework for Biological Invasions proposed by Blackburn et al. 2011). The risk analysis framework is currently being implemented in South Africa in an effort to underpin national regulatory lists of invasive species.

Abstract The aim of the present review is to discuss the roles of vitamin K and vitamin K-dependent proteins, and the combined action of the vitamins K and D, for maintenance of bone health. The most relevant vitamin K-dependent proteins in this respect are osteocalcin and matrix Gla-protein (MGP). When carboxylated these proteins appear to have the ability to chelate and import calcium from the blood to the bone, thereby reducing the risk of osteoporosis. Carboxylated osteocalcin appears to contribute directly to bone quality and strength. Adequate vitamin K status is required for carboxylation of MGP and osteocalcin. In addition, vitamin K acts on bone metabolism by other mechanism, such as a ligand for the nuclear steroid and xenobiotic receptor (SXR). In this narrative review, we examine the evidence for increased bone mineralization through dietary adequacy of vitamin K (phylloquinone or menaquinone). Summarizing the evidence for a synergistic effect of vitamin K and vitamin D3 we find that an adequate supply of vitamin K on top of an optimal vitamin D status seems to add to the benefit for maintaining bone health. More research related to synergism and possible mechanisms of vitamins D3 and K interaction in bone health is needed.

The conservation of coastal wetland ecosystems, like mangrove forests and salt marshes, represents a critical strategy for mitigating atmospheric emissions and climate change in the 21st century. Yet the existence of these environments is threatened by human-induced disturbances, namely deforestation and accelerated sea-level rise. Coastal systems maintain surface elevation in response to sea-level rise through a combination of physical and biological processes both above and below the ground surface. The quantification and relative contribution of belowground process controls (e.g., seasonal water content, organic matter decomposition) on surface elevation change is largely unexplored but crucial for informing coastal ecosystem sustainability. To address this knowledge deficit, we integrated measurements of surface elevation change of the live root zone (0.5 to 1 m depth) with geotechnical data from co-located sediment cores in the Sundarbans mangrove forest (SMF) of southwest Bangladesh. Core data reveal that the primary belowground controls on surface elevation change include seasonal fluctuations in pore-water content and the relative abundance of fine-grained sediments capable of volumetric expansion and contraction, supporting an elevation gain of ~2.42 ± 0.26 cm yr⁻¹. In contrast to many mangrove environments, the soils of the SMF contain little organic matter and are dominantly composed (>90%) of inorganic clastic sediments. The mineral-rich soil texture likely leads to less compaction-induced subsidence as compared to organic-rich substrates and facilitates surface equilibrium in response to sea level rise. Despite a relatively high soil bulk density, soil carbon (C) density of the SMF is very low owing to the dearth of preserved organic content. However, rates of C accumulation are balanced out by locally high accretion rates, rendering the SMF a greater sink of terrestrial C than the worldwide mangrove average. The findings of this study demonstrate that C accumulation in the SMF, and possibly other alluvial mangrove forests, is highly dependent on the continued delivery of sediment to the mangrove platform and associated settings.

:Peat substrate is made from peat from drained peatlands, which is a limited resource. A realistic estimate is that 50% of the world’s wetlands have been lost. Peat is used in horticulture, especially for the cultivation of vegetables in greenhouses. The consequences of peatland exploitation are an in-crease in the greenhouse effect and a decrease in carbon stocks. Wood fiber can be used as an al-ternative to peat. The chemical properties of the growing media interact and change continuously due to the small volume of the growing media, which is limited by the growing container. This study aims to gain new knowledge on the impact of nutrient changes in the microbial degradation of carbon compounds in wood fiber and mixtures with peat substrate on the content and uptake of nutrients required by plants. Cucumbers (Cucumis sativus L.) variety “Dirigent H” developed in the Netherlands, were cultivated in growing media of peat substrate and wood fiber: (1) peat substrate (PS); (2) wood fiber (WF); (3) wood fiber and peat substrate 50/50 v/v (WF/PS 50/50); (4) wood fiber and peat substrate 25/75 v/v (WF/PS 25/75). The rates of fertilization were the following: (1) con-ventional fertilization (CF); (2) N13; (3) N23; (4) N30. The experiment was carried out in three repli-cations. As the amount of wood fiber increased, the humidity and pH of the growing medium in-creased. Fertilization of cucumbers with different rates of nitrogen influenced the nutrient uptake. Plants grown in 50/50 and 25/75 growing media had the best Cu uptake when fertilized with N23. When the plants grown in the wood fiber media and the 50/50 media were fertilized with N13, N23, and N30, the Mn content in the growing media at the end of the growing season was significantly lower than the Mn content in the media with conventional fertilization. Thus, nitrogen improved the uptake of Mn by plants grown not only in the wood fiber but also in combinations with peat sub-strate. Growing plants in wood fiber and fertilizing them with N13 can result in optimum uptake of micronutrients. The number and mass of cucumber fruits per plant were influenced by the amount of wood fiber in the growing media and the application of nitrogen fertilizer. The highest number of fruits and mass of fruits per plant was obtained significantly higher when cucumbers were grown on WF/PS 50/50 growing media with additional N13 fertilization.

: Greenhouse plants are usually grown in containers filled with growing media. Peat is the main component of the growing media used in greenhouses. However, peat substrate is made from peat from drained peatlands, which is a limited resource. Wood fiber can be an alternative to use as a growing media for controlled-climate crops. The chemical properties of the growing media interact and change continuously due to the small volume of the growing media, which is limited by the vegetative container. Therefore, this study aims to gain new knowledge on the impact of nutrient changes in the microbial degradation of carbon compounds in wood fiber and mixtures with peat substrate on the content and uptake of nutrients required by plants. Cucumbers (Cucumis sativus L.) were cultivated in growing media of peat substrate and wood fiber: (1) peat substrate (PS); (2) wood fiber (WF); (3) wood fiber and peat substrate 50/50 v/v (WF/PS 50/50); (4) wood fiber and peat substrate 25/75 v/v (WF/PS 25/75). The rates of fertilization were the following: (1) conventional fertilization (CF); (2) N13; (3) N23; (4) N30. Fertilization of cucumbers with different rates of nitrogen influenced the nutrient uptake. Plants grown in WF/PS 50/50 and WF/PS 25/75 growing media had the best Cu uptake when fertilized with N23. When the plants grown in the wood fiber media and the 50/50 media were fertilized with N13, N23, and N30, the Mn content in the growing media at the end of the growing season was significantly lower than the Mn content in the media with conventional fertilization. Thus, nitrogen improved the uptake of Mn by plants grown not only in the wood fiber but also in combinations with peat substrate. Growing plants in wood fiber and fertilizing them with N13 can result in optimum uptake of micronutrients. The number and mass of cucumber fruits per plant were influenced by the amount of wood fiber in the growing media and the application of nitrogen fertilizer. The highest number of fruits and mass of fruits per plant was obtained significantly higher when cucumbers were grown on WF/PS 50/50 growing media with additional N13 fertilization.

This study evaluated the physicochemical, mineralogical properties, mobile chemical species’ bioavailability and translocation in Brassica juncea and Spinacea oleracea L plants of a South African coal fired power utility. Coal fly ash (CFA) disposal is associated with various environmental and health risks including air, soil, surface and ground water pollution due to the leaching of toxic chemical species; these ends up in food webs affecting human health, while repeated inhalation causes bronchitis, silicosis, hair loss and lung cancer. The morphology, chemical, and mineralogical composition of CFA were determined using Scanning Electron Microscopy (SEM), X-ray fluorescence (XRF) and X-ray Diffraction, respectively. In pot culture experiments, S. oleracea L and B. juncea plants were grown in three sets of pots containing CFA (set 1), soil (set 2) and a mixture of CFA plus soil at ratio 1:1 (50% CFA: 50% soil) (set 3), while no plants were grown in set 4 as a control for the leachate samples. SEM showed that surface morphology of CFA has a lower degree of sphericity with irregular agglomerations of many particles. The XRF results revealed that CFA contains 43.65%, 22.68% and 10.89% of SiO₂, Al₂O₃ and Fe₂O₃ respectively which indicate that the CFA is an alumino-silicate material. While XRD showed that the coal CFA contains mullite as a major phase followed by quartz mineral phases. Chemical species such as Fe, Mn, B, Ba and Zn were accumulated highly in most parts of the plant species. However, B. juncea showed higher potential to accumulate chemical species as compared to S. oleracea L. The bioconcentration and translocation factors (BF and TF) showed that B. juncea was the most effective in terms of bioconcentration and translocation of most of the chemical species. This indicates that B. juncea has potential in application for phytoremediation of CFA dumps and could contribute to remediation of CFA dumps and reduction of potential health and environmental impacts associated with CFA.

This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.

The $k$-means problem is to compute a set of $k$ centers (points) that minimizes the sum of squared distances to a given set of $n$ points in a metric space. Arguably, the most common algorithm to solve it is $k$-means++ which is easy to implement, and provides a provably small approximation factor in time that is linear in $n$. We generalize $k$-means++ to support: (i) non-metric spaces and any pseudo-distance function. In particular, it supports M-estimators functions that handle outliers, e.g. where the distance $\mathrm{dist}(p,x)$ between a pair of points is replaced by $\min {\mathrm{dist}(p,x),1}$. (ii) $k$-means clustering with $m\geq 1$ outliers, i.e., where the $m$ farthest points from the $k$ centers are excluded from the total sum of distances. This is the first algorithm whose running time is linear in $n$ and polynomial in $k$ and $m$.

Background: Participation is low (43%) in Australia’s National Bowel Cancer Screening Program which provides a biennial Fecal Immunochemical Test kit mailed to the home of Australians aged 50-74 years. While several factors for non-participation have been identified, the role of mental and physical health on screening behaviour has not been assessed. Methods: Participants of the Australasian Colorectal Cancer Family Registry Cohort were asked to complete a questionnaire on their colorectal cancer screening in the past five years and a validated measure of mental and physical health. The association between mental and physical health and screening was determined for Australian participants aged 45-75 years who had never been diagnosed with colorectal cancer. Multivariable logistic regression was used to adjust for measured potential confounders. Results: Of the 1130 eligible participants, 819 reported colorectal cancer screening in the past five years (72%). After adjusting for potential confounders, there was no evidence that overall mental or physical health was associated with colorectal cancer screening. However, one specific scale, general health, was positively associated with colorectal cancer screening (p=0.014) with those reporting higher levels of general health undergoing screening.Conclusion: We found limited evidence that mental and physical health, as measured by a short questionnaire, are associated with colorectal cancer screening. A potential limitation is reverse causation where previous screening may have impacted mental or physical health. A more detailed study of physical and mental health as barriers or enablers of screening is needed.

Sudden cardiac arrest (SAC) is a leading cause of mortality. In recent years, though, many lives have been saved through the use of defibrillators by random bystanders. As a result, defibrillators have been popping up in various locations. In this work we propose a state mandate demanding the presence of a defibrillator at every pharmacy store and argue that it could greatly enhance and augment the impact of the defibrillator network. We support our claim via detailed calculations of the immediate (a defibrillator readily available) and partial (a defibrillator available at a reasonable distance) population coverage achievable by the implementation of our proposal, taking the Argolis area in Peloponnese Greece as a case study. Our findings indicate that, in addition to other benefits, the use of pharmacies to establish a defibrillator network could bring an eight-fold increase in the immediate population coverage of defibrillators and would provide immediate / partial access to a defibrillator to 18 / 24 incidents of SCA per year that would otherwise lead to death, potentially leading to the saving of numerous lives. Alternative scenaria are also discussed.

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

Recent data suggest that K-Ras4B (hereafter K-Ras) can drive cancer cell stemness via calmodulin (CaM)-dependent, non-canonical Wnt-signalling. Here we examined whether another Ca2+-binding protein, the CaM-related centrin1, binds to K-Ras and could mediate some K-Ras functions that were previously ascribed to CaM. While CaM and centrin1 appear to distinguish between peptides that were derived from their classical targets, they both bind to K-Ras in cells. Cellular BRET-data suggest that CaM engages more with K-Ras than centrin1 and that the interaction with the C-terminal membrane anchor of K-Ras is sufficient for this. Surprisingly, binding of neither K-Ras nor its membrane anchor alone to CaM or centrin1 is sensitive to inhibition of prenylation. In support of an involvement of the G-domain of K-Ras in cellular complexes with these Ca2+-binding proteins, we find that oncogenic K-RasG12V displays increased engagement with both CaM and centrin1. This is abrogated by addition of the D38A effector-site mutation, suggesting that K-RasG12V is held together with CaM or centrin1 in complexes with effectors. When treated with CaM-inhibitors the BRET-interaction of K-RasG12V with centrin1 was also disrupted in the low micromolar range, comparable to that with CaM. While CaM predominates in regulating functional membrane anchorage of K-Ras, it has a very similar co-distribution with centrin1 on mitotic organelles. Given these results, a significant overlap of the CaM- and centrin1-dependent functions of K-Ras is suggested.

This qualitative research explores the transformative impact of technological innovations on retail marketing effectiveness. Through thematic analysis of interviews with industry professionals, the study identifies key themes including enhanced customer engagement, personalized marketing strategies driven by AI and machine learning, data-driven decision-making leveraging big data and analytics, the rise of e-commerce platforms, integration of mobile technologies, adoption of augmented reality (AR) and virtual reality (VR), development of omnichannel strategies, and implementation of loyalty programs. These themes illustrate how technology has reshaped retail marketing by enabling real-time customer interactions, personalized shopping experiences, and targeted marketing campaigns that enhance customer satisfaction and loyalty. The findings highlight the critical role of digital platforms like social media in fostering community engagement and brand loyalty. AI and machine learning facilitate personalized marketing efforts, tailoring product recommendations and enhancing customer experiences. Data-driven strategies optimize marketing ROI by providing insights into consumer behavior and market trends. E-commerce platforms expand market reach and streamline transactions, while mobile technologies and AR/VR innovations create immersive shopping experiences that captivate consumers. Omnichannel strategies integrate physical and digital channels, delivering seamless brand experiences and enhancing customer retention through CRM systems and loyalty programs. Looking ahead, retailers must navigate challenges such as rapid technological change, the need for continuous adaptation, and concerns about data privacy. By embracing technological advancements strategically and maintaining ethical standards, retailers can sustain competitive advantage, meet evolving consumer expectations, and drive growth in the digital era.

The human body supports the growth of a wide array of microbial communities in various niches, such as the oral cavity, gastro-intestinal and urogenital tracts and on the surface of the skin. These host associated microbial communities include yet-un-cultivable bacteria and are influenced by various factors. Together, these communities of bacteria are referred to as the human microbiome. Human oral microbiome consists of both symbionts and pathobionts. Deviation from symbiosis among the bacterial community leads to “dysbiosis”—a state of community disturbance. Dysbiosis occurs due to many confounding factors that predispose to a shift in the composition and relative abundance of microbial communities. Dysbiotic communities have been a major cause for many microbiomes related systemic infections. Such dysbiosis is directed by certain important pathogens called the “keystone pathogens” that could modulate community microbiome variations. One such persistent infection is oral infection, mainly periodontitis, where a wide array of causal organisms has been implied to systemic infections such as cardio vascular disease, diabetes mellitus, rheumatoid arthritis and Alzheimer’s disease. The keystone pathogens co-occur with many yet-cultivable bacteria and their interactions lead to dysbiosis. This has been the focus of recent research. While immune evasion is one of the major modes that lead to dysbiosis, new processes and new virulence factors of bacteria have been shown to be involved in this important process of that determine disease or health state. This review focuses on such dysbiotic communities, their interactions and their virulence factors that predispose the host to other systemic implications.

Derelict asbestos mine sites in South Africa pose a considerable risk to human, environmental and socio-economic health. Comprehensive mineralogical and geochemical datasets for the existing hazardous geological materials still exposed in Southern African derelict asbestos mines remain largely non-existent, as very little published and up-to-date literature is available. In this study three representative types of asbestos mineral fibres from derelict asbestos mines in Southern Africa, namely chrysotile from Havelock mine, amosite from Penge mine and crocidolite from Prieska mine are characterized mineralogically and geochemically to critically evaluate real-life hazards in rural and asbestos-fibre contaminated regions. The samples were examined using polarising light microscopy, X-ray fluorescence (major and trace elemental analysis), X-ray diffraction (including Rietveld refinement), specific surface area analyses and bio-durability tests. Data are discussed in view of their potential toxicities on both human health and the environment in the context of developing countries. Finally, information on the mineralogical and geochemical status of asbestos mine waste and its importance as baseline data for rehabilitation considerations is also evaluated.

Background and objectives: Chronic Kidney Disease and Mineral Bone Disorders (CKD-MBD) are frequently associated with an increased risk of both Vascular Calcifications (VCs) and bone fractures (BFs). The complex pathogenesis of VCs and BFs involves various factors such as calcium overload, phosphate imbalance, and secondary hyperparathyroidism. Key players, such as the vitamin K-dependent proteins (VKDPs) matrix Gla protein (MGP) and osteocalcin (BGP), have pivotal roles both for VCs and BFs. The Vitamin K Italian (VIKI) dialysis study, highlighted that hemodialysis patients treated with calcimimetics had higher levels of total BGP and MGP as compared to those untreated, suggesting a potential protective effect of these drugs on BFs and VCs, beyond the beneficial effect to reduce PTH levels. Methods: ETERNITY-ITA is a multi-center, comparative effectiveness, observational, longitudinal study that will enroll 160 hemodialysis patients (80 patients treated with Etelcalcetide and 80 age and sex-matched patients treated with Calcitriol or vitamin D analogs). Nephrologists will tailor the target dose of Etelcalcetide on individual level to achieve the KDIGO PTH target. In the Etelcalcetide-treated group, the addition of calcitriol will be allowed when required by clinical practice (for correction of hypocalcemia). Conclusion: The study will evaluate the real-world effect of Etelcalcetide on VKDP levels, such as BGP and MGP, at 3, 9, and 18 months from baseline. The resulting preservation of vascular and bone health will be assessed for the first time by examining aortic and iliac artery calcifications and vertebral fractures, respectively. Keywords: bone fractures; etecalcetide; hemodialysis; vascular calcification; vitamin K

The adoption of the Internet of Things (IoT) in supply chain operations has emerged as a pivotal advancement, offering the potential to significantly enhance operational efficiency, visibility, and responsiveness. This study explores the multifaceted impact of IoT integration in supply chains, emphasizing both the opportunities and challenges associated with its implementation. Through a combination of semi-structured interviews, case studies, and a comprehensive literature review, the research uncovers how IoT technologies contribute to improved asset tracking, real-time inventory management, and optimized logistics. Key benefits include enhanced operational efficiency, predictive maintenance capabilities, and better supply chain collaboration, which collectively lead to increased customer satisfaction and cost savings. However, the study also identifies several challenges, including the high costs of IoT infrastructure, difficulties in integrating new technologies with existing systems, and concerns over data security and privacy. These challenges pose significant barriers to adoption, particularly for smaller organizations. Despite these obstacles, the study highlights the substantial opportunities presented by IoT, such as fostering innovation, developing new business models, and enhancing supply chain resilience. The findings suggest that while the path to successful IoT adoption may be complex and resource-intensive, the potential benefits make it a valuable investment for organizations seeking to remain competitive in a rapidly evolving market. Addressing the identified challenges while leveraging the advantages of IoT will be crucial for companies aiming to optimize their supply chain operations and achieve long-term success.

The advent of 5G technology represents a transformative leap in the realms of retail marketing and supply chain operations, offering unprecedented enhancements and posing unique challenges. This study explores the multifaceted impacts of 5G, focusing on its influence on customer experiences, operational efficiency, and supply chain management. The integration of 5G enables advanced technologies such as augmented reality (AR) and virtual reality (VR), significantly enhancing the interactivity and personalization of the shopping experience. Real-time data collection and analysis, facilitated by 5G, allow for more precise inventory management and optimized store operations, leading to improved customer satisfaction and reduced operational costs. In the supply chain domain, 5G technology offers enhanced visibility and control through real-time tracking and environmental monitoring, which improves logistics efficiency and product quality management. Despite these advancements, the adoption of 5G technology involves considerable challenges, including high infrastructure costs, data security concerns, and global disparities in technology deployment. Businesses must navigate these obstacles while leveraging the benefits of 5G to achieve competitive advantage. The study also highlights the role of 5G in integrating with other advanced technologies, such as artificial intelligence (AI) and blockchain, to drive further innovation. Additionally, 5G supports sustainability efforts by optimizing energy use and reducing waste. Overall, the successful implementation of 5G requires strategic investment and adaptability, but it holds the potential to revolutionize retail and supply chain operations, enhancing efficiency, customer engagement, and sustainability in a rapidly evolving digital landscape.

We propose a conceptual model for the closeness parameter $\epsilon$, which characterizes exotic compact objects (ECOs). To estimate $\epsilon$, a thin spherical dust shell is considered, which gravitationally contracts from a specific position $r(t_{0})$ to near its gravitational radius $r(t_{2})=r_{s} + \epsilon$, in a finite time $t_{2}$, measured in the frame of a fiducial observer (FIDO). For an external observer, the shell’s kinematics is characterized by two clearly distinguishable phases: one of rapid contraction, where the shell is far away from the gravitational radius, $r(t_{0})\gg r_{s}$, and a second phase quasi-stationary, $r(t)\sim r_{s}$, where all of the shell’s mass is concentrated around the associated horizon, such that for a FIDO, a black hole (BH)is undistinguishable from a shell configured as a black shell (BS). \\ In the semi-classical approximation $E\ll \kappa_{0}l_{p}^{2}$ and tends to zero when the observation time of collapse $t_{2}$, measured by FIDO, tends to infinity; $\kappa_{0}$ and $l_{p}$ are surface gravity and Planck length, respectively. The quantum effects are significant when $\epsilon\ll r(t_{2})$ and $\epsilon$ tends to $\kappa_{0}l_{p}^{2}$. \\ Without knowing details on quantum gravity, parameter $\epsilon$ is calculated, which, in general, allows distinguishing the ECOs from BHs. Specifically, a BS (ECO) is undistinguishable from a BH.

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to currently not provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed `quantum-inspired' algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.

Vitamin K occupies a unique and often obscured place among its fellow fat-soluble vitamins. Evidence is mounting however, that vitamin K (VK) may play an important role in the visual system apart from the hepatic carboxylation of hemostatic related proteins. However, to our knowledge, no review covering the topic has appeared in the medical literature. Recent studies have confirmed that matrix Gla protein (MGP), a vitamin K-dependent protein (VKDP), is essential for the regulation of intraocular pressure in the mouse. The PREDIMED (Prevención con Dieta Mediterránea) study, a randomized trial involving 5860 adults at risk for cardiovascular disease, demonstrated a 29% reduction in the risk of cataract surgery in participants with the highest tertile of dietary vitamin K1 (PK) intake compared with the lowest tertile. However, the specific requirements of the eye and visual system (EVS) for VK, and what might constitute an optimized VK status, is currently unknown and largely unexplored. It is the intention therefore of this narrative review to provide an introduction concerning VK and the visual system, review ocular VK biology, and to provide some historical context to recent discoveries. Potential opportunities and gaps in current research efforts will be touched upon, in the hope of raising awareness, and encouraging continued VK related investigations in this important and highly specialized sensory system.

Clustering, or otherwise known as cluster analysis, is a learning problem that takes place without any human supervision. This technique has often been utilized, much efficiently, in data analysis, and serves for observing and identifying interesting, useful, or desired patterns in the said data. The clustering technique functions by performing a structured division of the data involved, in similar objects based on the characteristics that it identifies. This process results in the formation of groups, and each group that is formed, is called a cluster. A single said cluster consists of objects from the data, that have similarities among other objects found in the same cluster, and resemble differences when compared to objects identified from the data that now exist in other clusters. The process of clustering is very significant in various aspects of data analysis, as it determines and presents the intrinsic grouping of objects present in the data, based on their attributes, in a batch of unlabeled raw data. A textbook or otherwise said, good criteria, does not exist in this method of cluster analysis. That is because this process is so different and so customizable for every user, that needs it in his/her various and different needs. There is no outright best clustering algorithm, as it massively depends on the user’s scenario and needs. This paper is intended to compare and study two different clustering algorithms. The algorithms under investigation are k-mean and mean shift. These algorithms are compared according to the following factors: time complexity, training, prediction performance and accuracy of the clustering algorithms.

This research investigates the economic resilience of small and medium enterprises (SMEs) during financial crises, focusing on the factors that enable these businesses to survive and recover from economic downturns. Employing a qualitative methodology, the study examines various aspects of SME operations, including financial management, innovation, leadership, government support, geographical location, social capital, and contingency planning. The findings reveal that effective financial management—encompassing cash flow management, debt restructuring, and diversified funding sources—is crucial for maintaining stability during financial crises. Innovation and adaptability also play significant roles, with SMEs that embraced new technologies and market strategies demonstrating greater resilience. Leadership emerged as a critical factor, with leaders who communicated effectively and managed stress contributing to their organization's ability to navigate crises. Government support provided essential relief, although its impact varied depending on accessibility and timeliness. Geographical location influenced resilience, with urban SMEs having better access to resources, while rural SMEs benefited from strong community networks. Social capital, including relationships with suppliers and customers, was vital for providing stability and support. Contingency planning and risk management were identified as essential for preparing for and responding to disruptions. The research highlights that resilience is a multifaceted attribute resulting from the interplay of financial prudence, adaptability, effective leadership, supportive policies, geographic factors, and strong social networks. These insights offer valuable lessons for SME owners, policymakers, and support organizations, emphasizing the need for a comprehensive approach to building and sustaining resilience in the face of economic challenges.

This research explores the impact of 5G technology on supply chain efficiency and marketing innovations, highlighting its transformative potential and associated challenges. The study delves into how 5G enhances real-time tracking and monitoring within supply chains, significantly improving inventory management, reducing stockouts and overstocking, and ensuring optimal conditions during product transit. The advanced communication capabilities of 5G enable seamless collaboration among supply chain partners, facilitating quicker decision-making and improved risk management. Additionally, the integration of 5G technology with data analytics has optimized processes, improved forecasting accuracy, and led to cost reductions, showcasing the value of data-driven decision-making. In the marketing domain, 5G technology has driven the development of immersive augmented reality (AR) and virtual reality (VR) applications, enhancing customer engagement through interactive and experiential content. Personalized and targeted marketing strategies, supported by real-time data and deeper customer insights, have led to higher customer satisfaction and increased sales. The proliferation of connected devices, such as smart speakers and wearables, has further enabled personalized interactions and convenience, fostering brand loyalty and driving growth. Despite these advancements, the implementation of 5G presents challenges, including significant infrastructure investment, cybersecurity risks, and privacy concerns. Companies must navigate these issues by investing in robust security measures and developing comprehensive policies. The research concludes with an optimistic outlook on the future impact of 5G technology, emphasizing its potential to drive innovation, enhance operational efficiency, and transform customer experiences, ultimately shaping the future of business operations.

This research explores the pivotal role of big data analytics in enhancing retail marketing and supply chain optimization. As the retail industry undergoes rapid digital transformation, big data analytics has emerged as a critical tool for gaining competitive advantage and improving operational efficiency. The study delves into the various applications of big data analytics, including customer segmentation, personalized marketing, demand forecasting, inventory management, dynamic pricing, and supply chain optimization. It highlights how retailers leverage data from diverse sources to gain insights into consumer behavior, market trends, and operational performance, leading to more informed decision-making. The findings underscore the significant benefits of big data analytics in enhancing customer experiences through targeted engagement and personalized service. Additionally, the research addresses the ethical considerations and challenges associated with data privacy, security, and potential biases in analytics models. It emphasizes the importance of maintaining high standards of data quality and ethical practices to ensure the responsible use of data. The conclusion reiterates the transformative impact of big data analytics in the retail sector, suggesting that while it offers substantial advantages, careful management of its challenges is crucial. The study provides valuable insights into how retailers can harness the power of big data analytics to drive growth, innovation, and long-term success in an increasingly competitive and data-driven market environment.

With the rapid development of the Internet industry, the problem of information overload has arisen due to the abundance of information available online. Recommendation algorithms, as the core of recommendation systems, have been attracting much attention and are a hot topic of research for many experts and scholars. The classical recommendation algorithms are mainly divided into three major categories: collaborative filtering recommendation algorithms, content-based recommendation algorithms, and hybrid-based recommendation algorithms. Although these algorithms are widely used in various fields, with the proliferation of information, these traditional recommendation algorithms are no longer able to meet the needs of the times. To address this issue, recommendation systems have been developed to provide users with personalized and relevant information or products. Despite the wide use of recommendation algorithms, such as collaborative filtering, content-based filtering, and hybrid approaches, traditional recommendation algorithms have limitations and are no longer suitable for meeting the demands of the times. This paper proposes a new recommendation algorithm, SFRRG, that fuses structure and feature information in graph neural networks to improve the performance of the recommendation system in rating prediction. The effectiveness of the proposed algorithm is demonstrated through experiments on various data sets and compared with existing recommendation algorithms.

Sleep disorders have become a public health problem for many reasons. The aim of this systematic review is to describe social, economic, and environmental determinants of sleep disturbances in Australia and New Zealand. A total of 22 studies, 14 from Australia and 8 from New Zealand involving children, adolescents and adults are included in the review. In summary, we found that sleep health disparities among children and adolescents in both countries are strongly associated to parental SES. Overall, lower family SES such as low parental income, low parental education, low SES area index are associated with higher sleep disturbances among children and adolescents. In adults, results revealed that SES determinants such as low income, lower education, unemployment, and night work were associated with sleep disturbances. Specifically, unemployment was associated with poor sleep quality, insomnia, and increased likelihood of suboptimal sleep efficiency. Low income and lower education were associated with high risk of OSA, longer sleep duration, insomnia, higher odds of suboptimal sleep duration and sleep onset latency.

Sugarcane (Saccharum spp.) is a crop of great industrial and alimentary importance, essential for the production of numerous products. Given its significance, genetic improvement programs exist that involve a rigorous study process, from material selection to the development of new varieties, requiring at least seven selection phases. This study modeled the growth curve of the sucrose percentage (SP) in 33 hybrids and six control varieties (MEX 69-290, ITV 92-1424, CP 72-2086, COLMEX 94-8, COLMEX 95-27, RB 85-5113) during the plant and ratoon periods in the experimental fields of the Melchor Ocampo Sugar Mill, Jalisco, Mexico. For clustering the materials, k-means, k-medoids, and Density-based spatial clustering of applications with noise (DBSCAN) algorithms were used, considering four maturity types among the control varieties. The DBSCAN algorithm proved to be the most effective, as the means between groups were not statistically equal. The hybrids identified as candidates for subsequent phases due to their high SP were COSTA JAL, ATEMEX 99-48, ATEMEX 99-1, ATEMEX 99-61, MEX 70-486, MEX 80-1521, ITSAMEX 07-44814, ITSAMEX 06-6395, and ITSAMEX 07-1903. These results are crucial for improving the productivity and sustainability of the crop, with significant implications for the sugar industry.

In the particular configuration of the scalar field K-essence in the Wheeler-DeWitt quantum equation, for some age in the Bianchi type I anisotropic cosmological model, a fractional differential equation for the scalar field arises naturally. The order of the fractional differential equation is $\beta=\frac{2\alpha}{2\alpha - 1}$. This fractional equation belongs to different intervals, depending on the value of the barotropic parameter; when $\omega_{X} \in [0,1]$, the order belongs to the interval $1\leq \beta \leq 2$, and when $\omega_{X}\in[-1,0)$, the order belongs to the interval $0<\beta \leq 1$. In the quantum scheme, we introduce the factor ordering problem in the variables $(\Omega,\phi)$ and its corresponding momenta $(\Pi_\Omega, \Pi_\phi)$, obtaining a linear fractional differential equation with variable coefficients in the scalar field equation, then the solution is found using a fractional power series expansion. The corresponding quantum solutions are also given. We found the classical solution in the usual gauge N obtained in the Hamiltonian formalism and without a gauge. In the last case, the general solution is presented in a transformed time $T(\tau)$, however in the dust era we found a closed solution in the gauge time $\tau$.

The main objective of this paper is to introduce k-fractional derivative operator by using the definition of k-beta function. We establish some results related to the newly defined fractional operator such as Mellin transform and relations to k-hypergeometric and k-Appell's functions. Also, we investigate the k-fractional derivative of k-Mittag-Leffler and Wright hypergeometric functions.

The paper is dedicated to studying the influence of annealing temperature on the chemical, structural and electrophysical properties of low-k films made from porous organosilicate glasses containing terminal methyl groups. The films were deposited using centrifugation, followed by drying at 200 °C and annealing at temperatures ranging from 350 °C to 900 °C. Changes in refractive index, porosity, pore size, chemical composition, and electrical properties as a function of annealing temperature are evaluated. The results of JV measurements indicate that the methyl-modified film, which was annealed at 450 °С, demonstrates minimal leakage currents. This phenomenon can be explained by the almost complete removal of surfactant residues and non-condensed silanols, as well as the minimal thermal degradation of methyl groups. The results of the study on leakage current models in methyl-modified films, obtained at various annealing temperatures, lead us to conclude that the predominant mechanism of charge carrier transfer is Poole–Frenkel emission.

The continued application of organochlorine pesticides (OCPs) in indoor residual spraying has posed significant threat to human health in Northern part of South Africa, despite its ban. In this study, we investigated the occurrence and spatial distribution of DDTs and its metabolites in surface soil (30 samples) collected in and around the spray homesteads; demarcated into three concentric zones A, B, C in Tshilamusi Vhembe district, South Africa. DDTs were the most abundant of all the OCPs chemicals found in soil samples. The concentrations of DDT range from 12.19 to 65.69 µg/kg, with the highest occurring at zone A which is the zone of application. DDTs and all its metabolites considered in this study were found in appreciable concentrations in soil of Zones B and C, far from spray sites. The results showed a strong correlation between distance and concentration levels of DDTs and its metabolites. However, there is a need for proper monitoring of OCPs chemicals in other environmental matrices as well as relevant agencies enforcing strict adherence to regulations on consumption of OCPs.

We review time expansion wave theory and refine our model showing it to accurately match observed cosmological luminosity data. The theory augments general relativity to create a container within which quantum effects can be explained by treating time as an artefact of a fourth spatial dimension that we observe as the expansion of the universe. This leads us to explain mass creation, the speed of light limit, gravity, black holes without singularities and other macroscopic effects. Our model leads to an expansion velocity of 6.87 0.36 × 106 ms-1, which results in a Hubble expansion rate constant of 71.3 3.7 km/s/Mpc. Because of the finite window of time in which we exist, an extra a degree of freedom is available leading to interesting interpretations at the subatomic level. At this size range, then a spin is allowed within the time dimension explaining why wave particle duality occurs and explains the electromagnetic force and other quantum mechanical properties. It provides a solution to the double slit conundrum and can explain how quantum entangled partners can behave in a quantum way and pass seemingly time-defying information. The potential quantitative origin of the fine structure constant which governs the size of the electromagnetic force is also strongly indicated by this theory.

This study determined the temporal variation of PM2.5 in ambient air in Thohoyandou and further assessed the associated health risks. The levels of PM2.5 were quantified for a period of 1 year (April 2017-April 2018) using the gravimetric method. There was no significant difference (P-value = 0.18) in concentrations of both PM2.5 samples collected during weekdays (11.29 µg.m-3) and weekends (9.86 µg.m-3). However, higher concentrations of PM2.5 were measured in spring and the lowest was measured in summer. The cancer risk obtained for PM2.5 (2.21 × 10−5, 3 × 10-4, and 5 × 10-4 for infants, children, and adults respectively) in the outdoor air of Thohoyandou has exceeded the limit values by the USEPA and WHO, implying a significant risk for the whole population. For non-carcinogenic risks, the HQ values were 2.60, 4.81, and 2.60 for infants, children, and adults respectively. The HQ value &gt;1 indicates a non-carcinogenic risk to the residents in Thohoyandou and a higher risk to children. Moreover, PM2.5 in Thohoyandou is responsible for 0.15% and 0.13% of deaths resulting from cardiovascular disease and lung cancer respectively for adults above 30 years. PM2.5 is causing adverse health effects in Thohoyandou as deduced from the health risk assessment. Therefore, it is recommended that further epidemiological studies be conducted in Thohoyandou to estimate the burden of disease due to exposure to particulate matter and suitable controlling policies be arranged to reduce particulate matter.

Nanoporous membranes promise energy-efficient water desalination. Hexagonal boron nitride (h-BN), like graphene, exhibits outstanding physical and chemical properties, making it a promising candidate for water treatment. We employed Car–Parrinello molecular dynamics simulations to establish accurate modeling of Na+ and Cl- permeation through hydrogen passivated nanopores in graphene and h-BN membranes. In these complex systems the classical potentials cannot properly account for the physics at play. We demonstrate that ion separation works well for the h-BN by imposing a barrier of 0.13 eV and 0.24 eV for Na+ and Cl- permeation respectively, compared to the graphene where the Cl- ion faces a negative barrier of 0.68 eV and is prone toward blockade and Na+ permeation is associated with a slightly negative barrier of 0.03 eV. From the change in the solvation shell, we get the following trend: Na/h-BN > Na/G > Cl/G > Cl/h-BN. We argue that the trend in the permeation barrier changes to Cl-h-BN > Na-h-BN > Na-G > Cl-G due to a combination of several interactions, including the distortion of the water network induced by the ions, the ion-water interaction, and the ion-nanopore interaction. Overall, the desalination performance of h-BN surpasses that of their graphene counterparts.

Background: Students in the United States gain weight significantly during their first year of university, however limited data are available for Australian students. Methods: This 12-month observational study was conducted to monitor monthly body weight and composition, as well as quarterly eating behaviours, dietary intake, physical activity, sedentary behaviours, and basal metabolic rate changes amongst first-year Australian university students. Participants were first-year university students over 18 years. Results: Twenty-two first-year university students (5 males and 17 females) completed the study. Female students gained weight significantly at two, three, and four-months (+0.9 kg; +1.5 kg; +1.1 kg, p <0.05). Female waist circumference (2.5 cm increase at three-months, p = 0.012), and body fat also increased (+0.9%, p = 0.026 at three-months). Intakes of sugar, saturated fat (both >10% of total energy), and sodium exceeded recommended levels (>2000 mg) at 12-months. Greater sedentary behaviours were observed amongst male students throughout the study (p <0.05). Conclusions: Female students are at risk of unfavourable changes in body composition during the first year of university, while males are at risk of increased sedentary behaviours. High intakes of saturated fat, sugars, and sodium warrant future interventions in such a vulnerable group.

The visualization of hyperspectral images in display devices, having RGB colour composition channels is quite difficult due to the high dimensionality of these images. Thus, principal component analysis has been used as a dimensionality reduction algorithm to reduce information loss, by creating uncorrelated features. To classify regions in the hyperspectral images, K-means clustering has been used to form clusters/regions. These two algorithms have been implemented on the three datasets imaged by AVIRIS and ROSIS sensors.

Concept Knowledge (C-K) theory has been used in engineering and science-based research for more than a decade. Design of an Information Technology (IT) artefact is mostly pragmatic in nature. Design Science Research (DSR) methodology applied and studied in many Information Systems (IS) research. Many sub design decisions involved through the design of an IT artefact from a concept (idea) to a working prototype. A DSR artefact is based on a combination of decisions made during several sub-design stages. Artefacts are built based on the selection of elements in each sub-design space. Recording the design decisions on each sub-design space would be beneficial for future researchers. By knowing the design decisions on each sub-design space, researchers would be able to try different combinations of the design. C-K theory provides the ability to capture the design processes’ several sub-design spaces. In this paper, we discuss the DSR research methodology by looking at the stages proposed in the literature, and the application of C-K theory in an IT-based DSR. This paper also proposed a C-K theory-based protocol called Concept Tree for tracking and reporting artefact design steps. The application of C-K theory in DSR is exhibited using the implementation of the Concept Tree for a prototype design IT artefact.

In this paper, some convexity properties and some inequalities for the (p,k)-analogue of the Gamma function, Г_p,k(x) are given. In particular, a (p,k)-analogue of the celebrated Bohr-Mollerup theorem is given. Furthermore, a (p,k)-analogue of the Riemann zeta function, ζ_p,k(x) is introduced and some associated inequalities are derived. The established results provide the (p,k)-generalizations of some known results concerning the classical Gamma function.

Kamchatka berries (Lonicera caerulea L.) are known for their high content of phenolic compounds or vitamins, which is reflected in their antibacterial, detoxifying and anti-inflammatory properties. They are used in the production of jams, juices, wines and as natural dye. A bioferment was prepared from the pomace of the Kamchatka berry varieties Aurora and Indigo Gem and the strains Saccharomyces cerevisiae and Lacticaseibacillus paracasei, which was used to prepare a cosmetic preparation with a concentration of 5% in accordance with the guidelines of the COSMOS certification body. Conducted physico-chemical and organoleptic analyses and bioactive profile characterisation (UHPLC-DAD and UHPLC-ESI-MS/MS). The results showed that the presence of caffeic acid 4.47 ± 0.07 mg/100g was detected after fermentation of Kamchatka berry pomace. Vitamin C content increased by 141% after fermentation of Kamchatka berry pomace. The results of stability tests showed that in the process of physicochemical analysis of cosmetic preparation with bioferment of Kamchatka berry pomace the pH should oscillate in the range of 4.38-4.76 ± 0.01. Stability tests showed that the cosmetic should be protected from high temperatures and UV radiation and proved that the product is stable under changing conditions, resulting in lower transport and storage costs.

The current study aims to produce a material that has a dual effect of healing and anti-inflammatory. For this target k-carrageenan/polyacrylamide hydrogel film loaded with cetrimide (k-CAR/PAAm/CI) was performed by manual casting technique. Definite concentrations of k-CAR and AAm was heated at 80°C for 2 hours, CI and glycerol were added. The solution was cast without using initiator and cross-linker. The reaction of the sulfonic acid group -SO3H of k-CAR with -CONH2 group of PAAm lead to the formation of a sulfonamide (-SO2NH-) linkage. The characteristics of the produced films were investigated by FTIR, TGA, contact angle, and mechanical properties. Improvement in the thermal stability was performed in k-CAR/PAAm/CI2 film containing 1.5% CI compared to the film has 0.5% CI (k-CAR/PAAm/CI1). The contact angle measurement proved the films are hydrophobic that enhanced by increasing CI content. The tensile strength and elongation percent values are considered adequate for materials used in wound care. k-CAR/PAAm/CI2 (1.5% CI) film showed superior antimicrobial activity against P. aeruginosa, moderate activity against S. aureus, and low activity against E. coli. The film k-CAR/PAAm/CI2 was effectively inhibiting the heat-induced hemolysis and may offer a promising approach for the development of effective wound dressings.

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of dual-phase-lag heat conduction model with the Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag-Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease. Technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which include RT-PCR, antigen-antibody and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include Deep Learning and Transfer learning approach. The review also provide comparison between these 2 emerging technologies and open research issues for the development of smart-IoMT-enable platform for the detection of COVID-19.

The energy transition to wind and solar opens opportunities for green hydrogen as the wind and solar generation tends to bring electricity prices down to very low levels. This paper evaluates whether green hydrogen can integrate well with wind and solar PV to help with improved management of the South Australian electricity grid. Green hydrogen can use membrane electrolysis plants during surplus renewable energy periods. This hydrogen can then be electrified or used in industry. The green hydrogen system was analysed to understand the financial viability and technical impact of integrating green hydrogen. We also used systems engineering techniques to understand the system holistically, including the technical, social, environmental, and economic impacts. The results show opportunities for the system to provide seasonal storage, grid firming, and reliability services. Financially, it would need changes to electricity rules to be viable, but without those changes, our modelling suggested it would be financially viable within this decade

Sado island in Niigata prefecture is among the first GIAHS designated sites in Japan and among developed countries worldwide. Recent studies have pointed out the need to incorporate culture and farmer opinions to further strengthen GIAHS inclusivity in rural farming. In connection to this, the study explored whether farmer visibility, which is highlighted by GIAHS designation, actually translates to farmers’ actual perception of GIAHS involvement. A survey was conducted among Sado island farmers to determine their knowledge and perception of their GIAHS involvement, in connection to their perspectives on youth involvement, Sado island branding, and tourism management. Results showed that 56.3% of Sado island farmers feel uninvolved or unsure towards GIAHS, which is in stark contrast with the prevalent farming method in the area which is special farming (complies with GIAHS regulations). Further analyses revealed that farmers who feel that GIAHS does not promote youth involvement, Sado island branding, and tourism management have higher predisposition to perceive themselves as uninvolved towards GIAHS. This study highlights the need for careful reevaluation and integration of farmer insights and needs to the current GIAHS implementation in Sado island and in other GIAHS as well.

While lidar-based forest inventory methods have been widely demonstrated, prediction of tree diameters with lidar is not well understood. The performance metrics typically used in studies for prediction of diameters can be difficult to interpret and may not support comparative inferences between sampling designs or study areas. We evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions using two indices which are easier to interpret and compare. The indices are based on the coefficient of determination (R²), and root mean square deviation (RMSD). These indices facilitate comparisons with alternative (non-lidar) inventory strategies, and with other project areas. We evaluate k nearest neighbors (k-NN) dbh density (relative frequency by dbh class) prediction strategies with lidar for 190 training plots distribute across the 800 km² Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. Amongst the examined strategies we found Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.

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

This study delves into the impact of artificial intelligence on students’ learning.The paper pioneers the use of kmeans classification to reduce the dimensionality of features before the classification process, which minimizes redundant information and computational overhead. This step is not commonly found in prior research.Firstly, a random forest model is established with “whether AI assistance was used to complete the paper” as the output, and various features as inputs. The analysis of the predictive accuracy when changing input features reveals the degree of influence of each feature on using AI to complete the paper. After feature selection, the remaining 6 features can still effectively predict the use of AI to complete the paper, indicating the representativeness of the extracted indicators. Subsequently, the PCA-K-means algorithm is employed to classify the use of artificial intelligence. PCA algorithm is used for dimensionality reduction of the extracted features to better characterize students’ use of AI. Three representative variables are selected, and the contribution of each feature is computed based on the factor loading matrix. Finally, the K-means algorithm is used to classify students based on the three dimensional features, showing that students can be effectively classified into 2 categories. The results indicate that the majority of students support the use of AI technology to enhance their abilities, although some students may only intend to use AI to cope with exams and similar tasks.

The primary purpose of this research article is to study the famous K$\ddot{o}$the's Conjecture for the special cases - when a ring $\mathfrak{R}$ is commutative, and when $\mathfrak{R}$ is an Algebra over a field. Furthermore, some open problems involving tensor product of Algebras over a field has also been discussed extensively along with their proofs. Subsequently, an equivelent statement of K$\ddot{o}$the's problem in terms of algebra of matrices has been established.

(1) Background: Vitamin K deficiency is a common feature of chronic kidney disease (CKD), leading to impaired bone quality and increased risk of vascular calcifications. A method to indirectly measure Vitamin K status is measuring the blood level of Vitamin K dependent proteins (VKDP)- osteocalcin (OC) and matrix GLA protein (MGP). The aim of this study is to correlate the level of OC and MGP with markers of CKD mineral bone disorder (CKD-MBD). (2) Methods: We conducted a single-center cross-sectional study that included 45 CKD G5D patients and measured blood biochemistry, complete blood count and intact osteocalcin and matrix GLA protein. (3) Results: We found a strong statistically significant correlation of OC with the markers of CKD-MBD such as: iPTH , serum calcium and serum phosphorus and a strong indirect statistically significant correlation with abdominal circumference. There was also a statistically significant correlation of MGP with markers of inflammation (CRP). Higher levels of MGP were found in patients treated with vitamin K antagonists, non-calcium based phosphate binders and vitamin D receptor activator- paricalcitol. (4) Conclusions: In our study, we found that vitamin K deficiency, measured indirectly using the level of VKDP is associated to CKD-MBD. The use of medication such as phosphate binders, that reduce vitamin K absorbtion, Vitamin D that increases Vitamin K requirements and also vitamin K antagonists, seem to have an influence on the blood level of VKDPs.

We know that the classical Mittag-Leffler function play an important role as solution of fractional order differential and integral equations. We introduce the j-generalized p - k Mittag-Leffler function. We evaluate the second order differential recurrence relation and four different integral representations and introduce a homogeneous linear differential equation whose one of the solution is the j-generalized p-k Mittag-Leffler function. Also we evaluate the certain relations that exist between j-generalized p - k Mittag-Leffler function and Riemann-Liouville fractional integrals and derivatives. We evaluate Mellin-Barnes integral representation of j-generalized p-k Mittag-Le er Function. The relationship of j-generalized p-k Mittag-Leffler Function with Fox H-Function and Wright hypergeometric function is also establish. we obtained its Euler transform, Laplace Transform and Mellin transform. Finally we derive some particular cases.

Unmanned aerial vehicles serving as air base stations (UAV-BSs) are used in telecommunications, when the network is overloaded or unavailable, providing access to users on the ground. Due to its dynamics of providing access to networks in different altitudes, the electromagnetic signal of UAVs suffers losses along the path, thus facing several problems related to transmission, such as: attenuation, fading and distortion. In this work is proposed a 3D positioning strategy for UAVs, taking into account height, coverage radius and flight energy efficiency. Results show that the proposed algorithm is able to position the UAVs in suboptimal locations, providing network access for mobile users, maximizing the number of connected devices in the UAV-BS and saving energy, thus obtaining better performance than the approaches conventional.

Sado Island in Niigata prefecture, Japan is one of the first Globally Important Agricultural Heritage Systems (GIAHS) among developed countries and has since been involved in environmental conservation agriculture (ECA). While ECA is still in its early stage in Japan, it has proven to be effective in mitigating climate change in the agricultural sector; hence, this study analyzed the factors which could contribute to the ECA continuation among Sado Island farmers. The data revealed the prevalence of farmers’ cognitive dissonance between ECA and its mitigating effects on climate change. Exploratory factor analysis and ordinal regression confirmed the importance of perceived GIAHS involvement in the continuation of ECA. In addition, other identified factors affecting ECA continuation fall either on a macro-level (i.e., farmers’ awareness of their role in improving their environment) or micro-level (i.e., farmers’ differing farm optimizations). These perspectives highlighted the altruistic nature of the Sado Island ECA farmers by valuing the improvement of their local and global environment as their main reason to continue ECA, whereas their various farm management optimizations support this observed farmer altruism by providing avenues to increase yield with only a moderate paddy land area. This study thereby highlights the need to continuously develop sustainable strategies to maintain and improve a positive farmer mindset towards ECA.

This paper explores the application of the generalized double reduction method to the (1+1)-dimensional Kaup-Boussinesq (K-B) system, which models nonlinear wave propagation. Double reduction method is a structured and systematic approach in the analysis of partial differential equations (PDEs). We first identify the Lie point symmetries of the K-B system and construct four non-trivial conservation laws using the multiplier method. The association between the Lie point symmetries and the conservation laws is established, and the generalized double reduction method is then applied to transform the B-K system into second-order differential equations or algebraic equations. The reduction process allowed us to derive two exact solutions for the K-B system, illustrating the method’s effectiveness in handling nonlinear systems. This work highlights the effectiveness of the generalized double reduction method in simplifying and solving nonlinear nonlinear systems of PDEs, contributing to a deeper understanding of the systems.

While the global COVID-19 pandemic has been widely acknowledged to affect the mental health of health care workers (HCWs), attention to measures that protect those on the front lines of health outbreak response has been limited. In this cross-sectional study, we examine workplace contextual factors associated with how psychological distress was experienced in a South African setting where a severe first wave was being experienced with an objective of identifying factors that can protect against HCWs experiencing negative impacts. Consistent with mounting literature on mental health effects, we found a high degree of psychological distress (57.4% above General Health Questionnaire cut-off value) and a strong association between perceived risks associated with the presence of COVID-19 in the healthcare workplace and psychological distress (adjusted OR = 2.35, p <.01). Our research indicates that both training (adjusted OR 0.41, 95% CI 0.21 – 0.81) and the reported presence of supportive workplace relationships (adjusted OR 0.52, 95% CI 0.27 – 0.97) were associated with positive outcomes. This evidence that workplace resilience can be reinforced to better prepare for the onset of similar outbreaks in the future suggests that pursuit of further research into specific interventions to improve resilience is well merited.

Salinity, resulting from climate change and excessive mineral fertilization, burdens farmers and negatively impacts soil and water ecosystems in the Northeastern Nile Delta. Organic and biological approaches are crucial for addressing these issues. This study examined the effects of individual and combined inoculations with cyanobacteria, yeast, and Arbuscular Mycorrhizal Fungi (AMF), with or without K-humate, on enhancing salinity tolerance, increasing yield, and improving crop quality of wheat and faba bean. The aim was to reduce NPK mineral fertilizer application rates to 25% of recommended doses. An economic evaluation was also conducted. In a preliminary laboratory experiment, the interactive effects of these microbiomes on the production of plant antioxidant enzymes and soil enzymes were investigated under salinity stress. Results showed that co-inoculation, especially with K-humate, yielded superior outcomes compared to individual inoculations. The laboratory experiment findings were confirmed by a field trial conducted in saline-alkaline soil in the Northeastern Nile Delta region. The study found that the integrated inoculation of cyanobacteria, yeast, and mycorrhizae with K-humate, along with 25% mineral NPK fertilization enhanced microbial activity, including AMF colonization, spore density, and total bacteria, cyanobacteria, and yeast counts in the plant rhizosphere. It also increased soil enzyme activity, improving soil fertility compared to individual or mineral control treatments. This integration resulted in wheat yields of 2.77 tons/ha and faba bean yields of 4.75 tons/ha. Crop quality improved, with higher levels of nitrogen, potassium, phosphorus, and protein in wheat grains and faba bean seeds. Microbiomes inoculation increased potassium uptake over sodium, enhancing the potassium/sodium ratio and salt stress tolerance. This approach reduces reliance on costly mineral fertilizers by up to 75%, enabling economically viable cultivation in marginal lands. These sustainable practices optimize resource utilization, maximize economic efficiency, and preserve natural resources.

Real time condition monitoring and precision health assessment system is a mandatory need for effective maintenance program in industrial sector. Rapid advancement in the information technology and other engineering technologies have invited more proactive attention from research and development in industrial sectors and particularly in condition monitoring of machines and related Industrial processes. In this work, drill bit condition monitoring techniques have been developed based on wavelet analysis and artificial neural networks (ANN) for automatic drill bit fault detection and classification. An experimental work has been conducted to capture the vibration signals for analysis. In this experiment, the CNC drill machine is used with high carbon steel drill bit and mild steel material as work piece. The cutting condition parameters are kept constant and the wear level is varied from 0.2 to 0.6 mm. The Data Acquisition system (DAQ) with Lab VIEW software is used to capture the vibration signals for drill bit with different wear conditions using accelerometer. The captured vibrations data are analyzed using continuous wavelet transform (CWT) with Morlet wavelet and Daubechies wavelet as a prime function. In general, the CWT coefficient is used to generate the inputs features to ANN for automatic tool condition classification, with two outputs (0, 1) for healthy and (1, 0) for faulty. The outcome of ANN showed 98% of accuracy in the wear prediction and this results show the effectiveness of the combed WT and ANN for automatic classification of tool wear conditions with high success rate.

Embracing Fourth Industrial Revolution(4IR) techniques is a need for a long-term sustainability and to do automation in the processes of Industrial and education sector. Rapid advancement in the information technology and other engineering technologies demand more proactive steps in the education institutes. In the post pandemic world, there is an increasing demand of changes in teaching and learning methodologies with the support of modern digital technologies. So, all of the teaching institutes need to have a significant step change in the areas of learning and teaching. The impact of 4 IR is more on the education sectors compared to previous generation’s industrial revolution. Keeping track of key technologies can be quite a challenge as they keep evolving at a very fast pace. Fourth Industrial revolution is the current technological digital revolution in which major focus is on cyber physical and biological systems such as Artificial Intelligence, Robotics, Internet on Things, Virtual Reality etc., The educators and institutions should establish the pedagogies and teaching with the aid of Industry 4.0 techniques to evolve education 4.0. So, it is very important to understand the challenges and competencies required about the implementation of 4IR by the faculty members and students who are currently studying in Higher Education Institutes (HEIs). This paper aim is to do a systematic review about industry 4.0 techniques and its application in relate to the online learning and teaching, change in pedagogies, enhancement of data management in HEIs, digital and virtual laboratories etc.

In this paper, closed forms of the sum formulas $\sum_{k=0}^{n}W_{k}^{2}$ \ for the squares of generalized Tetranacci numbers are presented. We also present the sum formulas $\sum_{k=0}^{n}W_{k+1}W_{k},$ $\sum_{k=0}^{n}W_{k+2}W_{k},$ and $\sum_{k=0}^{n}W_{k+3}W_{k}.$ As special cases, we give summation formulas of the of Tetranacci, Tetranacci-Lucas and some other fourth order linear recurrance sequences.

Children with hearing loss have been found to have significantly more behavioral and emotional challenges than their typically hearing peers, though these outcomes are variable at the individual level. Working memory deficits have been found to relate to executive functioning and overall emotion regulation, leading to behavior challenges. Language development is essential for development of social relationships and communicating one’s needs - this may lead to distress when children cannot communicate effectively. Based on prior findings in children with hearing loss and their typically hearing peers, working memory and language skills were hypothesized to be related to parent and teacher report of socioemotional functioning. Participants were 35 children with hearing loss (66% female, M = 5.17 years old, SD = ±1.97) whose language, working memory, and socioemotional functioning were evaluated during the course of treatment and educational planning. Bivariate analyses indicated that working memory was related to a number of socioemotional domains (e.g., functional communication, atypicality, withdrawal), as were language scores (e.g., social skills, inattention). The direction of these associations was such that stronger working memory and language skills were related to more regulated socioemotional functioning. A call to action of the current study includes more education with regard to profiles and presentations of children with hearing loss, and an early focus on socioemotional learning to foster the development of regulatory skills.

This qualitative research explores the impact of Artificial Intelligence (AI) on Customer Relationship Management (CRM), providing a comprehensive analysis of how AI technologies are transforming CRM practices. AI integration in CRM has revolutionized customer interactions, enabling enhanced personalization, efficiency, and customer engagement. By leveraging machine learning, natural language processing, and predictive analytics, organizations can create highly tailored customer experiences, automate routine tasks, and anticipate future customer needs with precision. The study identifies the significant benefits of AI, including improved customer insights, faster response times, and proactive engagement strategies. However, it also highlights the challenges associated with AI adoption in CRM, such as data privacy and security concerns, algorithmic bias, high implementation costs, and resistance to change. The findings emphasize the need for robust data protection measures, strategies to mitigate biases, significant investments in technology and training, and effective change management to overcome these challenges. The thematic analysis reveals key areas where AI is making an impact, including customer service, customer segmentation, and personalized marketing strategies. Additionally, the study discusses the organizational implications of AI adoption, such as enhanced decision-making, increased productivity, and the necessity for staff reskilling. Future directions for AI in CRM suggest further advancements in AI algorithms, greater integration with other business systems, enhanced customer experiences, and a focus on ethical AI practices. This research underscores the transformative potential of AI in CRM and provides insights for organizations aiming to leverage AI for improved customer relationship management.

This study explores the influence of social media marketing on brand loyalty, employing a qualitative research approach to gain in-depth insights into the key factors that drive consumer engagement and commitment. Through semi-structured interviews with 20 active social media users and a comprehensive analysis of social media content, the research identifies several critical elements affecting brand loyalty. Personalization emerged as a central theme, with tailored content, customized offers, and personalized recommendations significantly enhancing users' emotional connection with brands. High-quality content, characterized by engaging visuals and valuable information, was found to reinforce positive brand perceptions and sustain loyalty. Influencer marketing also plays a pivotal role, with authentic endorsements from relevant influencers boosting brand credibility and trust. Effective community management, involving active engagement, responsive feedback, and relationship-building, contributes to a positive brand experience and strengthens user loyalty. Targeted and relevant social media advertising supports brand loyalty, although excessive or irrelevant ads can lead to disengagement. The study also highlights the importance of balancing interaction frequency and content quality to maintain user interest without causing fatigue. Addressing social media fatigue by delivering relevant and valuable content is crucial for preventing user disengagement. Content that resonates with users' values and emotions further strengthens brand attachment. Consistency in brand voice, visual identity, and values ensures a coherent and positive brand experience. The findings emphasize the need for a strategic approach to social media marketing, integrating personalization, content quality, influencer partnerships, community management, advertising relevance, and consistency to build and sustain strong brand loyalty.

This study investigates the adoption of Robotic Process Automation (RPA) in marketing operations, exploring its impact on efficiency, data accuracy, and overall effectiveness. RPA has emerged as a transformative technology, automating routine tasks such as data entry, report generation, and campaign management, which has significantly enhanced operational efficiency within marketing departments. By automating repetitive processes, RPA enables marketing professionals to focus on strategic and creative tasks, resulting in accelerated campaign execution and improved productivity. Additionally, the integration of RPA with Customer Relationship Management (CRM) systems has led to more accurate and consistent data, providing valuable real-time insights that enhance decision-making and personalization in marketing strategies. Despite these benefits, the study also highlights several challenges associated with RPA adoption. These include concerns about workforce implications, such as job displacement and the need for reskilling, as well as implementation difficulties related to system compatibility and workflow design. Ethical and regulatory considerations, particularly regarding data privacy and compliance, are also critical factors that organizations must address to maintain customer trust and adhere to legal standards. The study further explores the potential of integrating artificial intelligence (AI) with RPA to drive future innovations in marketing automation. AI-driven RPA offers advanced capabilities, including predictive modeling and personalized marketing, which promise to enhance the effectiveness of automation strategies. The findings underscore the need for organizations to adopt best practices in RPA implementation and remain adaptable to technological advancements to fully leverage the benefits of automation and maintain a competitive edge in the dynamic marketing landscape.

Big data analytics has fundamentally transformed how organizations enhance customer experience by leveraging vast amounts of data to tailor interactions, streamline services, and drive strategic decisions. This research explores the multifaceted role of big data in improving customer experiences, focusing on its impact on personalization, customer service, operational efficiency, and strategic decision-making. By analyzing recent advancements and applications, the study highlights how big data analytics enables highly personalized recommendations, targeted marketing efforts, and dynamic content that significantly enhance customer engagement. Additionally, it examines the improvements in customer service through real-time support, sentiment analysis, and predictive assistance, which collectively contribute to a more responsive and customer-centric approach. The research also addresses how big data analytics optimizes operational efficiency by streamlining workflows, improving resource allocation, and reducing costs, thus enhancing overall organizational performance. Strategic decision-making benefits from data-driven insights into market trends, pricing strategies, and competitive positioning, allowing organizations to adapt and thrive in a competitive environment. However, the study also acknowledges the challenges associated with data quality, privacy, and investment, emphasizing the need for robust measures to address these issues. Furthermore, the integration of big data with emerging technologies such as artificial intelligence, the Internet of Things, and blockchain presents new opportunities and complexities in enhancing customer experience. Overall, this research underscores the transformative potential of big data analytics while highlighting the importance of addressing its associated challenges to fully capitalize on its benefits.

This study explores consumer perceptions of virtual reality (VR) in marketing, aiming to understand how immersive technology impacts engagement and brand perception. As VR continues to evolve and integrate into various marketing strategies, it becomes crucial to examine its effectiveness and the factors influencing consumer attitudes. Through a detailed thematic analysis, this research identifies key themes such as familiarity with VR technology, immersion and engagement, technical quality, accessibility, personalization, storytelling, social interaction, purchase intentions, and brand perception. The findings reveal that familiarity with VR enhances enthusiasm and comfort, leading to more positive attitudes towards VR marketing. The immersive and multi-sensory nature of VR provides a significant advantage over traditional advertising methods, fostering stronger emotional connections and increasing memorability. However, the quality of the VR experience is critical; high-quality graphics and seamless interactions are essential for maintaining positive consumer perceptions. Technical issues, such as lag and motion sickness, can negatively impact the experience, especially for those less acquainted with VR technology. Accessibility remains a challenge, with limited access to VR headsets restricting the reach of VR marketing campaigns. Personalization of VR content is highly valued, though it must be balanced with ethical considerations related to data privacy. The power of storytelling in VR enhances engagement by allowing consumers to actively participate in narratives. Social interaction within VR experiences adds an extra layer of enjoyment. The study also highlights the practical applications of VR marketing in high-involvement sectors and underscores the need for continuous innovation and consumer feedback to maximize the technology's impact. Overall, VR holds significant potential to transform marketing practices by offering immersive, engaging, and memorable experiences that deepen consumer connections and drive positive outcomes.

This study explores the role of chatbots in enhancing customer service, examining their benefits, limitations, and overall impact on modern support systems. Chatbots have emerged as a transformative technology in customer service, offering significant advantages such as increased efficiency through automation of routine tasks, reduced response times, and the ability to provide 24/7 support. By handling high volumes of inquiries and managing repetitive tasks, chatbots alleviate the workload of human agents, allowing them to focus on more complex issues. Additionally, chatbots employ advanced data analytics and machine learning to deliver personalized interactions, enhancing user engagement and satisfaction. Despite these benefits, the research identifies several challenges, including chatbots' difficulty in managing complex queries and their lack of emotional intelligence, which often necessitates escalation to human agents. The study also highlights the importance of well-designed chatbots and seamless integration with existing systems to ensure effective user experiences. Privacy and security concerns are critical, as chatbots handle sensitive customer data, requiring robust measures to protect against breaches and ensure compliance with data protection regulations. Looking ahead, advancements in technologies such as augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) are expected to further enhance chatbot capabilities, addressing current limitations and expanding their role in customer service. The findings suggest that a hybrid approach, combining chatbots with human support, offers the most effective solution for delivering high-quality customer service. Overall, chatbots have proven to be valuable assets, and their continued evolution promises to significantly impact the future of customer service.

This study investigates the influence of digital marketing on consumer purchasing decisions, focusing on how various strategies impact consumer behavior. By analyzing data collected from in-depth interviews and thematic analysis, the research highlights several key factors driving consumer engagement and purchases. Personalized content emerged as a significant factor, with tailored recommendations and customized email campaigns creating a sense of individual attention and enhancing consumer involvement. Social media platforms played a pivotal role, with advertisements, influencer endorsements, and user-generated content significantly affecting purchasing decisions. The impact of visual content, including high-quality images and videos, was also notable, as these elements helped consumers better understand products and increased their confidence in making purchases. Email marketing proved effective, particularly when offering exclusive discounts and creating a sense of urgency. Mobile marketing demonstrated its growing influence, with consumers valuing the convenience of mobile-friendly websites and app notifications. The integration of artificial intelligence further improved the shopping experience by providing instant assistance and personalized recommendations. Ethical considerations, such as data privacy and honest advertising, were essential for maintaining consumer trust and loyalty. User-generated content provided authentic insights that influenced purchasing decisions, while omnichannel strategies offered a seamless and flexible shopping experience. Gamification elements, including reward programs and interactive challenges, enhanced consumer engagement and motivation. The study concludes that a multifaceted approach to digital marketing, incorporating these strategies, is crucial for effectively engaging consumers and driving sales. The findings offer valuable insights for marketers aiming to optimize their digital marketing strategies in the evolving digital landscape.

This study explores the transformative impact of machine learning on predictive analytics within supply chain management. As supply chains become increasingly complex and dynamic, traditional methods of forecasting and management are proving inadequate. Machine learning offers advanced algorithms capable of analyzing vast datasets to uncover patterns and trends that enhance decision-making processes. This research investigates how machine learning improves demand forecasting accuracy, optimizes inventory management, strengthens supplier relationships, and enhances risk management. The study employs a qualitative approach, analyzing data from industry case studies and expert interviews to identify key themes and challenges associated with the adoption of machine learning. Findings reveal that machine learning significantly improves forecasting precision, reduces inventory costs, and enables proactive supplier and risk management. However, challenges such as ensuring data quality, acquiring specialized skills, integrating new technologies with existing systems, and addressing ethical considerations are critical. The study also highlights the potential of emerging technologies like blockchain, IoT, and 5G to further enhance machine learning applications in supply chain management. This research contributes to a deeper understanding of how machine learning can drive innovation and efficiency in supply chains, while also addressing the hurdles that organizations must overcome. The results underscore the need for a balanced approach that incorporates technological advancements and ethical practices to fully leverage the benefits of machine learning.

This study explores the impact of artificial intelligence (AI) on digital marketing strategies, emphasizing how AI technologies have transformed marketing practices. AI's ability to analyze vast amounts of data has significantly enhanced targeting and personalization, enabling marketers to deliver highly relevant and tailored content to specific consumer segments. The integration of AI in content creation has streamlined processes, improving efficiency while maintaining high standards of quality and relevance. Customer engagement has also been revolutionized through AI-driven tools such as chatbots and predictive assistance, which provide real-time, personalized support and enhance overall user satisfaction. Despite the numerous benefits, the adoption of AI in digital marketing presents challenges, including data privacy concerns, algorithmic bias, high implementation costs, and the complexity of integrating AI with existing marketing systems. The shortage of skilled professionals further complicates effective AI utilization. Looking forward, the potential for AI to drive innovation in digital marketing is substantial, with opportunities for advancements in personalization, predictive analytics, and the integration of emerging technologies like virtual and augmented reality. However, it is essential for businesses to navigate these developments carefully, addressing ethical considerations and ensuring responsible use of AI to maintain consumer trust and achieve sustained success. This study provides insights into the transformative effects of AI on digital marketing and offers a comprehensive overview of the associated benefits and challenges, highlighting the need for a balanced approach to leveraging AI technology.

This research explores the adoption of advanced analytics in supply chain decision-making, emphasizing its transformative impact on organizational efficiency and strategic advantage. The study investigates the key drivers behind the adoption of advanced analytics, including the growing complexity of data, the need for agility, and the pursuit of competitive advantage and cost reduction. It identifies significant challenges such as data integration difficulties, high implementation costs, and a shortage of skilled professionals, which organizations face when integrating these sophisticated tools. Despite these challenges, the research highlights the substantial benefits of advanced analytics, including enhanced forecasting accuracy, improved supply chain visibility, optimized processes, and cost savings. The findings indicate that while the implementation of advanced analytics varies across different sectors—such as retail, manufacturing, logistics, and healthcare—the overarching themes of improved efficiency and better decision-making are consistently observed. The study also discusses future trends, noting the integration of emerging technologies such as artificial intelligence, machine learning, and blockchain, which promise to further enhance analytics capabilities and accessibility. The successful adoption of advanced analytics relies not only on technological advancements but also on fostering a data-driven organizational culture, investing in continuous learning, and ensuring robust data governance. As supply chain management continues to evolve, advanced analytics will play a crucial role in driving strategic success and maintaining a competitive edge in a complex and dynamic market environment.