Nine crossbred finishing barrows randomly assigned to 3 dietary treatments were used to investigate the effects of dietary lysine on muscle growth related metabolic and signaling pathways. Muscle samples were collected from the longissimus dorsi of individual pigs after feeding the lysine-deficient, lysine-adequate, or lysine-excess diet for 5 weeks, and the total RNA was extracted afterwards. Affymetrix Porcine Gene 1.0 ST Array was used to quantify the expression levels of 19,211 genes. A total of 674 transcripts were differentially expressed (P ≤ 0.05); 60 out of 131 transcripts (P ≤ 0.01) were annotated in the NetAffx database. Ingenuity pathway analysis showed that dietary lysine deficiency may lead to (1) increased muscle protein degradation via the ubiquitination pathway as indicated by the up-regulated DNAJA1, HSP90AB1 and UBE2B mRNA, (2) reduced muscle protein synthesis via the up-regulated RND3 and ZIC1 mRNA, (3) increased serine and glycine synthesis via the up-regulated PHGDH and PSPH mRNA, and (4) increased lipid accumulation via the up-regulated ME1, SCD, and CIDEC mRNA. Dietary lysine excess may lead to (1) decreased muscle protein degradation via the down-regulated DNAJA1, HSP90AA1, HSPH1, and UBE2D3 mRNA, and (2) reduced lipid biosynthesis via the down-regulated CFD and ME1 mRNA.

Background/Objective: The Dark Web has played a pivotal role in the progress and sophistication of cybercrime. It provides an incubation network beyond the reach of traditional search engines where cybercriminals create and display exploit kits, offers illicit goods and services, and exchange confidential insider intelligence. Cybercriminals are extremely adept at selecting targets, applying tools to achieve their objectives, and minimizing red tape. The increasing sophistication of cybercriminals and the exponential rise of cybercrime against critical infrastructure underlines the necessity of identifying emerging threats. The objective of this research is to investigate the evolving threats within the Dark Web including crimeware-as-a-service and the integration of AI/ML into cyberattacks to inform risk management strategies and strengthen security measures.Research Problem: The exponential rise in cybercrime against critical infrastructure reflects growing sophisticationpresenting a significant challenge to organizations and society. The motivation behind cybercrime is fundamentally driven by self-greed which has contributed drastically to the magnitude and changes in methods used by cybercriminals to enhance profitability. The impact of cybercrime on business organizations presents an adverse impact on society and carries significant risks for the progress of individuals and the world at large. As cybercriminals adopt new technologies and services such as crimeware-as-a-service, identifying emerging trends becomes crucial to developing proactive strategies for the detection and prevention of cyber threats.Methodology: This research employs a systematic literature review approach to analyze emerging trends in cybercrime originating from the Dark Web. The review includes scholarly articles, news sources, and blog posts sourced from platforms like Google Scholar, IEEE Xplore, and various libraries. The key focus is to answer questions regarding the relationship between the Dark Web and cybercrime, accelerating cybercrime activities, and the benefits and implications of these new trends.Results: Key findings of this paper range from the rise of crimeware-as-a-service attacks and the increasing use of artificial intelligence and/or machine learning capabilities by cybercriminals to automate attacks across various businesses and organizations are also propounded along with information related to entry points and cybercrime attack pathways. The emergence of sophisticated cybercrime techniques, including ransomware-as-a-service, targeted attacks using AI, and exploitation of IoT vulnerabilities, are identified as critical trends. Social engineering, malware, and the rise of remote work have expanded the attack surface for cybercriminals.Discussion: As the use of cybercrime continues to metamorphose, the identification of new threats and extrapolation of emerging trends is critical to investigate the challenges associated with the monitoring and detection of illegitimate activities on the Dark Web as well as for the establishment of proactive risk management strategies and implementation of robust security measures. The research highlights the transformation of cybercrime into a structured and scalable ecosystem driven by technological advancements and service-based attack models. Cybercriminals now leverage AI/ML increasing the sophistication and success of their attacks. The commoditization of cybercrime has enabled less skilled individuals to participate amplifying the volume and diversity of threats faced by organizations.Conclusion: As cybercrime continues to evolve and adopt emerging technologies, organizations must remain vigilant and adaptable. The findings emphasize the need for proactive risk management, continuous monitoring of cybercrime trends, and robust security measures to mitigate the increasing threats originating from the Dark Web. Future research should focus on deeper exploration of AI-driven attacks and the development of more advanced countermeasures to safeguard critical infrastructure.

Aims: This study aimed at examining coach-athlete violence based on the retrospective reports of adults who had been athletes as children and adolescents; predict variables that contribute to the existence of such violence; describe difficulties that the participants encountered as young athletes following such violence; and reveal the outcomes of such violence on their emotions and behaviors, in the past and present. Methodology: The applied mixed-methods comprised quantitative self-reporting questionnaires and qualitative interviews. The former included 440 participants (mean age 27.6) who had trained for at least one year in a sports union youth department; the latter included 14 participants (aged 23-37). These competitive athletes came from eight branches of sports. The interviews were analyzed based on the Narrative Approach. Results: According to the quantitative study, all participants had experienced coach violence at least once during their career, mainly psychological violence and neglect, followed by physical violence. Sexual violence was least reported. The age of their retirement from sports and the number of coaches that they had had were significant predictors of violence. Thematic analysis of the qualitative interviews resulted in six types of coach-athlete violence: (1) psychological violence; (2) verbal violence; (3) starvation and food fattening; (4) non-proportional punishing; (5) physical violence; and (6) sexual violence. Conclusions: It is vital that coach-athlete violence is addressed in public discourse, that the topic of of young athletes’ safety is introduced into coaching education, and that a position holder is nominated to be in charge of such safety in all sports organizations.

While there is a coordinated effort around reaching zero dose children and closing existing equity gaps in immunization delivery, it’s important that there is agreement and clarity around how ‘zero dose status’ is defined and what is gained and lost by using different indicators for zero dose status. There are two popular approaches used in research, program design, and advocacy to define zero dose status: one uses a single vaccine to serve as a proxy for zero dose status, while another uses a subset of vaccines to identify children who have missed all routine vaccines. We provide a global analysis utilizing the most recent publicly available DHS and MICS data to compare the number, proportion, and profile of children 12 to 23 months who are ‘penta-zero dose’ (have not received the pentavalent vaccine), ‘truly’ zero dose (have not received the BCG, polio, pentavalent, and measles vaccines), and ‘misclassified’ zero dose children (those who are penta-zero dose but have received at least one other vaccine). Our analysis includes 194,829 observations from 82 low- and middle-income countries. Globally, 14.2% of children are penta-zero dose and 7.5% are truly zero dose, suggesting that 46.5% of penta-zero dose children have had at least one contact with the immunization system. While there are similarities in the profile of children that are penta-zero dose and truly zero dose, there are key differences between the proportion of key characteristics among truly zero dose and misclassified zero dose children, including access to maternal and child health services. By understanding the extent of connection zero dose children may have with the health and immunization system and contrasting it with how much the use of a more feasible definition of zero dose may underestimate the level of vulnerability in the zero dose population, we provide insights that can help immunization programs design strategies that better target the most disadvantaged populations. If the vulnerability profiles of the truly zero dose children are qualitatively different from that of the penta zero dose children, then failing to distinguish the truly zero dose populations, and how to optimally reach them, may lead to the development of misguided or inefficient strategies for vaccinating the most disadvantaged population of children.

The purpose of this paper is to show how the self-exciting threshold autoregressive (SETAR) model could be an appropriate econometric framework for characterizing the dynamics of the U.S. public debt/GDP ratio after the Bretton Woods collapse. Our preferred SETAR specifications are capable in capturing the main stylized facts of the U.S. public debt/GDP ratio between 1974 and 2024. In addition, the estimated SETAR models are consistent with several theoretical frameworks that seek to explain the behavior of the U.S. public debt/GDP ratio before and after the Global Financial Crisis (GFC). Finally, the paper provides some arguments on why the previous studies that use the exponential smooth threshold autoregressive (ESTAR) models, or SETAR-type models for the first differences of the U.S. public debt/GDP ratio, are potentially mis-specified, both on econometric and economic grounds.

As the world’s population age, Cognitive Frailty (CF) is becoming one of the most serious health problems and elucidating its biological mechanisms along with prevention and treatments becomes increasingly important also considering the associated health costs. This pilot study aims to investigate the potential cognitive and neuropsychological effects of a 5-week treatment with a therapy based on the regenerative properties of ozone (O3) in a cohort of patients stratified according to CF. We also study the potential effects of O3 on blood-based biomarkers indicative of specific biological systems that may be altered in CF. Seventy-five older persons have been recruited and randomly assigned to receive the active treatment (150cc of oxygen-O2-O3 mixture at the concentration of 30ug of O3 per cc of O2) or O2, or the placebo (air) for 5 weeks. The primary endpoint is a change in the scores of clinical scales from baseline (T0) to weeks 3 (T3), 9 (T9), 15 (T15) after treatment. The secondary endpoint is a change in biomarkers levels resulting from transcriptomics, proteomics and metabolomic patterns at the same times. This study will evaluate whether an intervention with the O2-O3 therapy could have potential cognitive, neuropsychological and biological effects on CF.

Reactive energetic plasticizers (REPs) coupled with hydroxy-telechelic poly(glycidyl azide-co-tetrahydrofuran) (PGT)-based energetic polyurethane (PU) binders for use in solid propellants and plastic-bonded explosives (PBXs) were investigated. The generation of gem-dinitro REPs along with a terminal alkyne stemmed from a series of finely designed approaches to not only satisfy the common demands as conventional energetic plasticizers but prevent the migration of plasticizers. The miscibility and rheological behavior of a binary mixture of PGT/REP with various REP fractions were quantitatively determined by differential scanning calorimetry (DSC) and rheometer, respectively, highlighting the promising performance of REPs in the formulation process. The kinetics on the distinct reactivity of propargyl vs. 3-butynyl species of REPs towards the azide group of the PGT prepolymer in terms of Cu-free azide-alkyne 1,3-dipolar cycloaddition (1,3-DPCA) was studied by monitoring 1H nuclear magnetic resonance spectroscopy and analyzing the activation energies (Ea) obtained using DSC. The thermal stability of the finally cured energetic binders with the incorporation of REPs indicated that the thermal stability of the REP/PGT-based PUs was maintained independently of the REP content. The tensile strength and modulus of the PUs increased with increasing the REP content. In addition, the energetic performance and sensitivity of REP and REP triazole species was predicted.

When limited area models of the hydrodynamics of the atmosphere and of the ocean are 1 run over an extended time, variability unrelated to external "drivers" emerge - this variability is 2 colloquially named "hydrodynamical noise" or just "noise". This article summarises what we have 3 learned in the past few years about the properties of such noise, and the implications for numerical 4 experimentation and analysis. The presence of this noise can be identified easily in ensembles of 5 numerical simulations, and it turns out that the intensity of the noise is closely linked to the scale- 6 dependent "memory". At the system level, this "memory" term as given by Hasselmann’s Stochastic 7 Climate model plays a key role. In the case of marginal seas, the process of baroclinic instability 8 modulated by tides and the formation of seasonal thermoclines are significant aspects. Some more 9 general aspects are discussed, such as the applicability of the Stochastic Climate model to systems 10 outside of atmospheric and oceanic dynamics, the irreversibility of tipping points and the challenges 11 of detecting changes beyond a noise level and of the attribution of causes of change.

Injuries to the anterior cruciate ligament (ACL) often result in post-traumatic osteoarthritis (PTOA). To better understand the molecular mechanisms behind PTOA development following ACL injury, we profiled ACL injury-induced gene expression changes in knee joints of three mouse strains with varying susceptibility to OA: STR/ort (highly susceptible), C57BL/6 (moderately susceptible) and super-healer MRL/MpJ (not susceptible). Right knee joints of the mice were injured using a non-invasive tibial compression injury model that closely mimics ACL rupture in humans and global gene expression was quantified before and at 1-day, 1-week, and 2-weeks post-injury using RNA-seq. Following injury, STR/ort displayed severe cartilage degeneration while MRL/MpJ had little cartilage damage. Gene expression analysis suggested that prolonged inflammation and elevated catabolic activity in STR/ort injured joints, compared to the other two strains may be responsible for the severe PTOA phenotype observed in this strain. MRL/MpJ had the lowest expression values for several inflammatory cytokines and catabolic enzymes activated in response to ACL injury. Furthermore, we identified several genes highly expressed in MRL/MpJ compared to the other two strains including B4galnt2 and Tpsab1 which may contribute to enhanced healing in the MRL/MpJ. Overall, this study has increased our knowledge of early molecular changes associated with PTOA development.

This study explores the development and characterization of ceramic rod reinforced TWIP (Twinning Induced Plasticity) steel matrix composites, fabricated using a lost foam casting process. The incorporation of ceramic rods aims to enhance the mechanical properties of TWIP steel, which is known for its excellent plasticity. Mechanical testing demonstrated a significant improvement in flexural strength and ductility, with the composite exhibiting over double the strength of the unreinforced TWIP steel. Heat treatment at 500°C for 60 minutes further enhanced these properties, increasing the composite's flexural strength to 1023 MPa and improving its ductility. The observed improvements are attributed to the synergistic effects of the hard ceramic reinforcement and the ductile matrix, as well as the microstructural modification conferred by heat treatment. These findings suggest that ceramic rod reinforced TWIP steel composites are promising materials for applications requiring a combination of high strength and ductility.

This study builds on our previous Systematic Literature Review (SLR) that assessed the applications and performance of zk-SNARK, zk-STARK, and Bulletproof Non-Interactive Zero-Knowledge Proof (NIZKP) protocols. To address identified research gaps, we designed and implemented a benchmark comparing these three protocols using a dynamic MiMC hash application. We evaluated performance across four general-purpose programming libraries and two programming languages. Our results show that zk-SNARK produced the smallest proofs, while zk-STARK generated the largest. In terms of proof generation and verification times, zk-STARK was the fastest, and Bulletproof the slowest. Interestingly, zk-SNARK proofs verified marginally faster than zk-STARK, contrary to other findings. These insights enhance our understanding of the functionality, security, and performance of NIZKP protocols, providing valuable guidance for selecting the most suitable protocol for specific applications.

(1) Background: Considering the autoimmune nature with genetic predisposition of alopecia areata, and the immunomodulatory properties of some probiotics, the present study was conducted in patients with alopecia areata. (2) Method: A 24-weeks randomized, double-blind, placebo-controlled clinical trial to evaluate the efficacy of an oral probiotic intake in severity of alopecia areata and the effect on skin and gut microbiota. 3) Results: The number of alopecia areata plaques decresed in 56% of patients in the probiotic group vs. 30% in the placebo group. Patients with improvement on the affected skin surface area, showed a results of 45% and 20% in probiotic and placebo groups, respectively. For "activity", "inactivity” and "regrowth", an improvement in 55%, 67% and 55% of patients was respectively observed in the probiotic group, compared to 50%, 40% and 30% in the placebo group. No changes were observed in the gut microbiota during the intervention period. Regarding skin microbiota, changes were detected in the probiotic group, with reductions in characteristic genera during the study. (4) Conclusions: To our knowledge, this is the first clinical trial assessing the efficacy of a probiotic in patients with alopecia areata. This probiotic mixture improves the course and modify the skin microbiota of these patients.

Addressing within field spatial variability for nitrogen management to avoid over and under-use of nitrogen is crucial for optimizing crop productivity and ensuring environmental sustainability. In this study we investigated the economic, environmental and agronomic benefits of variable rate nitrogen application in potato (Solanum tuberosum). An online visible and near infrared (vis-NIR) spectroscopy sensor we utilized to predict soil moisture content (MC), pH, total organic carbon (TOC), extractable phosphorus (P), potassium (K), magnesium (Mg), and cation exchange capacity (CEC) using a partial least squares regression (PLSR) models. The crop's normalized difference vegetation Index (NDVI) from Sentinel-2 satellite images was incorporated to online measured soil data to derive fertility management zones (MZs) maps, after homogenous raster and clustering analyses. The MZs maps were categorized into high fertile (VR-H), medium-high fertile (VR-MH), medium-low fertile (VR-ML), and low fertile (VR-L) zones. A parallel strip experiment compared variable rate nitrogen (VR-N) with uniform rate (UR) treatments, adjusting nitrogen levels based on fertility zones as: 50% less for VR-H, 25% less for VR-MH, 25% more for VR-ML, and 50% more for VR-L zones compared to the UR treatment. Results showed that the VR-H zone received a 50% reduction in N fertilizer input and demonstrated a significantly higher crop yield compared to the UR treatment. This implies a potential reduction in negative environmental impact by lowering fertilizer costs while maintaining robust crop yields. In total, the VR -N treatment received an additional 1.2 Kg/ha of nitrogen input, resulting in a crop yield increase of 1.89 tons/ha. The relative gross margin for the VR-N treatment compared to the UR treatment 374.83 €/ha, indicating substantial profitability for the farmer. To further optimize environmental benefits and profitability, additional research is needed to explore site-specific application of all farm resources through precision agricultural technologies.

LSU (RESPONSE TO LOW SULFUR) proteins belong to a plant-specific gene family initially characterized by their strong induction in response to sulfate (S) deficiency. However, little is known about the LSU gene repertoire and evolution of this family in land plants. In this work, a total of 270 LSU family members were identified using 134 land plant species with whole genome sequence available. Phylogenetic analysis revealed that LSU genes belong to a Spermatophyta-specific gene family, and their homologs are distributed in three major groups, two for dicotyledons and one group for monocotyledons. Moreover, we analyzed the expression of LSU genes in one representative species of each phylogenetic group (wheat, tomato and Arabidopsis) and found a conserved response to S-deficiency, suggesting that these genes might play a key role in S stress responses. Accordingly, Arabidopsis lsu2 knockout mutant plants showed increased levels of internal sulfate content and lower levels of expression of different key genes involved in S deficiency and metabolism like SDI2 and APR3. In summary, our results indicate that LSU genes are evolutionarily conserved in angiosperms and that specific members of this family might play an important role regulation of S transport and assimilation.

Pregnancy is associated with significant physiological changes, including alterations in lipid metabolism, which can have implications for both maternal and fetal health. This study aimed to evaluate serum lipid profile variations among healthy Sudanese women during the second and third trimesters of pregnancy. A total of 60 pregnant women, divided equally between the second and third trimesters, were compared with 30 non-pregnant, age-matched controls. Serum levels of total cholesterol, triglycerides, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) were measured using a BTS-Biosystem analyzer. The results revealed a statistically significant increase in total cholesterol, triglycerides, LDL, and HDL levels among pregnant women compared to the control group (p < 0.01). Additionally, lipid levels were significantly higher in the third trimester than in the second trimester. A weak positive correlation was found between parity and serum levels of total cholesterol, triglycerides, and LDL, while no significant correlation was observed for HDL. These findings suggest that pregnancy, particularly in the third trimester, is associated with substantial increases in serum lipid levels, highlighting the need for monitoring lipid profiles during pregnancy to manage potential risks of hyperlipidemia and its associated complications.

Potocki-Lupski Syndrome (PTLS) is a rare condition associated with a duplication of 17p11.2 that may underlie a wide range of congenital abnormalities and heterogeneous behavioral phenotypes. Along with developmental delay and intellectual disability, autism-specific traits are often reported to be the most common among patients with PTLS. To contribute to the discussion of the role of autism spectrum disorder (ASD) in the PTLS phenotype, we present a case of a female adolescent with a de novo dup(17)(p11.2p11.2) without ASD features, focusing on in-depth clinical, behavioral, and electrophysiological (EEG) evaluations. Among EEG features, we found the atypical peak-slow wave patterns and a unique saw-like sharp wave of 13 Hz that was not previously described in any other patient. The power spectral density of the resting state EEG was typical in our patient with only the values of non-linear EEG dynamics: Hjorth complexity and Fractal dimension were drastically attenuated compared with the patient’s neurotypical peers. Here we also summarize results from previously published reports of PTLS that point to the about 21% occurrence of ASD in PTLS that might be biased, taking into account methodological limitations. More consistent among PTLS patients were intellectual disability and speech and language disorders.

The first exposure to influenza is thought to impact subsequent immune responses later in life. The consequences of this can be seen during influenza epidemics and pandemics with differences in morbidity and mortality for different birth cohorts. There is a need for better understanding of how vaccine responses are affected by early exposures to influenza viruses. In this analysis of hemagglutination inhibition (HI) antibody responses in two cohorts of military personnel we noticed differences related to age, sex, prior vaccination, deployment and birth year. These data suggest that HI antibody production, in response to influenza vaccination, is affected by these factors. The magnitude of this antibody response is associated with, among other factors, the influenza strain that circulated following birth.

Feline infectious peritonitis (FIP) is a severe and invariably fatal disease affecting both domestic and wild felines with limited effective therapeutic options available. By considering the significant immunomodulatory effects of vitamin E observed in both animal and human models under physiological and pathological conditions, we have provided a full in-silico investigation of vitamin E and related compounds and their effect on the crystal structure of feline infectious peritonitis virus 3C-like protease (FIPV-3CLpro). This work revealed the β-tocotrienol and δ-tocotrienol analogues as inhibitor candidates for this protein, suggesting their potential as possible drug compounds against FIP or their supplementary use with current medicines against this disease.

This study aimed to develop a high-quality dry emulsion incorporating omega-3, 6, and 9 fatty acid-rich ostrich oil for use as a dietary supplement. Extracted from abdominal adipose tissues using a low-temperature wet rendering method, the ostrich oil exhibited antioxidant properties, favorable physicochemical properties, microbial counts, heavy metal levels, and fatty acid compositions, positioning it as a suitable candidate for an oil-in-water emulsion and subsequent formulation as a dry emulsion. Lecithin was employed as the emulsifier due to its safety and health benefits. The resulting emulsion, comprising 10% w/w lecithin and 10% w/w ostrich oil, was stable, with a droplet size of 3.93 ± 0.11 μm. This liquid emulsion underwent transformation into a dry emulsion to preserve the physicochemical stability of ostrich oil, utilizing Avicel® PH-101 or Aerosil® 200 through a granulation process. Although Aerosil® 200 exhibited superior adsorption, Avicel® PH-101 granules surpassed it in releasing the ostrich oil emulsion. Consequently, Avicel® PH-101 was selected as the preferred adsorbent for formulating the ostrich oil dry emulsion. The dry emulsion, encapsulated with a disintegration time of 3.11 ± 0.14 min for ease of swallowing, maintained microbial loads and heavy metal contents within acceptable limits. Presented as granules containing butylated hydroxytoluene, the dry emulsion showcased robust temperature stability, suggesting the potential incorporation of animal fat into dry emulsions as a promising dietary supplement.

Background: in this paper, a new physical penetration technology for transdermal administration with traditional Chinese medicine (TCM) characteristics - Fu’s cupping therapy (FCT) - was established and studied by in-vitro and in-vivo experiments; the penetration effect and mechanism of FCT physical penetration technology (FCT-PPT) was preliminarily discussed. Method: Indomethacin (IM) as a model drug，by transdermal in vitro tests the establishment of the high，medium and low reference were finished as the chemical permeation system；chemical penetration enhancers and iontophoresis as a reference，the percutaneous penetration effect of FCT for IM patch was evaluated with 7 species diffusion kinetics model and in vitro drug distribution；naproxen as an internal standard，using UPLC-MS/MS technology，the IM quantitative analysis method in vivo was established，and pharmacokinetic parameters (AUC0-t，AUC0-∞，AUMC0-t，AUMC0-∞, Cmax and MRT) as indicators were used evaluate to FCT penetration role in vivo；in the same time，the group used 3K factorial design to study joint synergistic penetration effect on FCT and chemical penetration enhancers (CPEs)；by SEM and TEM，the skin micro and ultrastructural changes of the stratum corneum (SC) surface were observed, to explore pay tank penetration mechanism. Results: In vitro and in-vivo skin permeation experiments revealed that both the total cumulative percutaneous amount and in-vivo percutaneous absorption amount (AUC and AUMC) of indomethacin that permeated SD mouse skin using FCT techniques were greater than the amount observed using CPE and iontophoresis: Firstly, in contrast to the control group, the indomethacin percutaneous rate (PR) of the FCT lower group (FCTL) was 35.52%, and the enhancement ratio (ER) at 9h was 1.76X, which was roughly equivalent to the penetration enhancing effect of the CPEs and iontophoresis; secondly, the indomethacin PR of the FCT middle (FCTM) group and the FCT high intensity group (FCTH) were respectively 47.36% and 54.58%, ER at 9h were separately 3.58X and 8.39X; thirdly, pharmacokinetic studies showed that in-vivo indomethacin percutaneous absorption of the FCTs was higher than that of the control group, that of the FCTM group was slightly higher than that of the CPEs group, and that of the FCTM group was significantly higher than that of the others. Meanwhile, the variance analysis indicated that the combination of the FCT penetration enhancement method and the CPE method had beneficial effects in penetration enhancing of the skin: the significance level of the CPE method was 0.0004, which was apparently lower than the 0.001, meaning the difference was markedly significant; the significance level of the FCT was under 0.0001, its difference markedly significant; and the significance level of factor interaction A×B was lower than 0.0001, indicating that its difference of the synergism was markedly significant. Moreover, SEM and TEM images showed that the SC surfaces of SD rats treaded with FCT-PPT was damaged, and hard to observe the complete surface structure with its SC pores growing bigger and its special “brick structure” becoming looser, indicating that it broke the barrier function of skin, which revealed potentially a major route of skin penetration. Conclusion: FCT, as percutaneous penetration new technologies, has penetration effects significantly, with Chinese characteristics and highly clinical value, worth promoting development.

The development of analog rice, apart from being an effort to diversify food, also has the potential to be develop as functional food in fulfilling the nutrients needed by a community. Katuk leaf is known for its ability to accelerate breast milk production for lactating women which is inseparable from sterol as bioactive content. This study aimed to determine the best formulation of analog rice made from cassava flour, banana flour, Katuk leaf powder and soy lecithin that was sensory acceptable, in a shape resembling rice, and able to fulfill the nutritional needs of lactating women. Analog rice was produced using extruder machine before the physical and sensory properties analyses were carried out, followed by the chemical properties analysis. Formulation C (80% cassava flour, 20% banana flour, 3% Katuk leaf powder and 0.5% soy lecithin) was obtained as the best or most preferred formulation based on sensory analysis which the grain shaped oval round resembling original rice and had green-brownish color, fluffy texture, distinct aroma and taste derived from the raw material used. Therefore, this research is expected to support the development of analog rice for providing the main staple food for fulfilling the lactating women’s nutrition.

The analysis of the key factors which prevent or contribute to the promotion and development of the Power Purchase Agreements (PPAs) in order to meet the sustainability goals represents an important issue which worth being investigated, especially in the situation in which entering in bilateral relations inside this contract has a powerful meaning in achieving new climate targets, developing and improving the use of green energy, and promoting a sustainable green economy. The main goal of this research is to identify and analyze the main factors which help or pre-vent the promotion and development of a PPA, in order to meet the sustainability goals by promoting a green economy. For this research survey based on comparative analysis, questionnaires and interviews with energy experts, Spearman’s correlation matrix and IBM SPSS Statistics for Window are also used. The results suggest that although there are positive interest and an open attitude towards the PPA from both sides (sellers and buyers), but there is still place for improvement, the lack of knowledge or the insufficient measures taken so far being two of the reasons way until now in Romania we do not have a developed PPA market and the level of knowledge regarding this type of mechanism is quite low.

A review discusses recent research findings spanning the last two decades concerning ultrasonic technologies applicable to the oil, gas, and coal sectors. Various experiments conducted in laboratories have demonstrated the efficacy, cost-effectiveness, and environmental friendliness of ultrasound in recovering and processing oil, bitumen, coal, and oil shale. Ultrasound enhances formation permeability, coal gas permeability, and oil viscosity, particularly when delivered in short, powerful pulses at medium frequencies. Combining ultrasound with traditional recovery methods has shown promising results, boosting recovery efficiency by up to 100%. At the same time, ultrasonic treatment reduces the use of traditional reagents, thereby reducing environmental pollution. Moreover, ultrasound treatment shows potential in tasks such as separating oil-water emulsions, desulfurization, dewaxing oil, coal enrichment, and extracting valuable metals from metal-bearing shales through hydrometallurgical leaching. However, widespread industrial implementation of ultrasonic technology necessitates further field and mathematical research.

The insufficient of fish meal has been become a prominent problem with the development of the aquaculture industry. Therefore, it is great significance that replacement fish meal using other protein sources for the sustainable development of aquaculture industry. In this study, fermented cottonseed meal (FCM) and fermented rapeseed meal (FRM) were used to replace fish meal in Chinese mitten crab. The ratio of FCM and FRM were set according to 1:2, 1:1, and 2:1, and were supplemented into 25% fish meal (FM-25) and 15% fish meal (FM-15) diets, respectively, to formulated 6 kinds of experimental diets. The feeding duration was 8 weeks. The results showed that the weight gain (WG) and specific growth rate (SGR) of crabs fed the diets containing 25% fish meal were significantly higher than crabs fed 15% fish meal diets. At 25% fish meal level, the WG and SGR of crabs fed the diet with 1FCM:1FRM were significantly higher than those fed the 1FCM:2FRM and 2FCM:1FRM diets. At 25% fish meal level, the crude lipid (CL) content of crabs fed the diet with 1FCM:2FRM were significantly higher than those fed the 2FCM:1FRM and 1FCM:1FRM. At 25% fish meal level, the activities of glutathione peroxidase (GPX) and peroxidase (POD) of crabs fed the diet with 1FCM:2FRM were significantly higher than those fed the 2FCM:1FRM and 1FCM:1FRM. At 25% fish meal level, the relative expression of peroxiredoxin (PRX) of crabs fed the diet with 1FCM:1FRM were significantly higher than those fed the 2FCM:1FRM and 1FCM:2FRM. In summary, diets supplemented with a mixture of fermented cotton meal and fermented rapeseed meal (1:1) can significantly improve the growth and antioxidant capacity of Chinese mitten crab.

Fusarium wilt (FW) caused by Fusarium oxysporum f. sp. Cucumerinum (Foc) of cucumber is a destructive soil-borne disease in cucumber (Cucumis sativus.L). There is limited knowledge on molecular mechanisms to FW resistance-mediated defense responses in cucumber. In this study, metabolome and transcriptome profiling were carried out in two FW, resistant (NR) and susceptible (NS), near isogenic lines (NILs) before and after Foc inoculation. The NILs showed consistent and stable FW resistances in multiple greenhouse and laboratory screening tests. Widely targeted metabolomic analysis identified differentially accumulated metabolites (DMAs) with significantly more accumulation in NR in response to Foc infection including many phenolic acid and flavonoid compounds from the flavonoid biosynthesis pathway. Transcriptome analysis identified differentially expressed genes (DEGs) between the NILs upon Foc inoculation including genes for secondary metabolite biosynthesis in or transcription factor genes regulating the flavonoid biosynthesis pathway. Joint analysis of the metabolomic and transcriptomic data identified DAMs and DEGs closely associated with biosynthesis of phenolic acid and flavonoid DAMs. The association of these compounds with NR-conferred FW resistance was exemplified by in vivo assays in which two phenolic acid compounds, bis (2-ethylhexyl) phthalate and diisooctyl phthalate, and the flavonoid compound gallocatechin 3-O-gallate were shown to have significant inhibitory effect on Foc growth. The antifungal effect of three compounds seems to be a novel finding. We conclude that phenolic acids and flavonoids play an important role in NR mediated FW resistance in cucumber.

Background: Although antiretroviral therapy (ART) effectively halt disease progression in HIV infections, complete eradiation of the virus remains elusive. Additionally, challenges such as long-term ART toxicity, drug resistance, and the demanding regimen of daily and lifelong ad-herence required by ART highlight the imperative need for alternative therapeutic and preventa-tive approaches. In recent years, broadly neutralizing antibodies (bNAbs) have emerged as promising candidates, offering potential for therapeutic, preventative and possibly curative in-terventions against HIV infection. Objective: This review aims to provide a comprehensive overview of the current state of knowledge regarding the passive immunization of bNAbs in HIV-1 infected individuals. Main findings: Recent findings from clinical trials have highlighted the potential of bNAbs in treatment, prevention, and in the quest of an HIV-1 cure. While monotherapy with a single bNAb is insufficient in maintaining viral suppression and preventing viral escape, ultimately leading to viral rebound, combination therapy with potent, non-overlapping epitope-targeting bNAbs have demonstrated prolonged viral suppression and delayed time to rebound by effec-tively restricting the emergence of escape mutations, albeit largely in individuals with bNAb-sensitive strains. Additionally, passive immunization of bNAb have provided ‘proof-of-concept’ for antibody-mediated prevention against HIV-1 acquisition, although com-plete prevention was not obtained. Therefore, further research on the use of bNAbs in HIV-1 treatment and prevention remains imperative.

One of the most common solutions to access underground is through the construction of circular shafts. At preliminary design stage analytical expressions are often used to estimate the horizontal earth pressure acting on the lining. However, most of these solutions are based on active state conditions and do not consider the restrictions imposed on the shaft lateral displacements by the rigidity of its lining or by the construction method employed. To overcome this limitation a new methodology to account with the shaft lateral displacements in the determination of the horizontal earth pressure acting on circular shafts is developed and validated against the results of relevant physical scale models. The obtained results show good agreement with the measured data, reproducing adequately the reduction of the horizontal earth pressure from its initial value to an active state condition with the increase of the shaft lateral displacement. Following this approach more realistic predictions of the earth pressure acting on the lining of circular shafts can be performed.

Our previous research provides evidence that vergence eye movements may significantly influence cognitive processing and could serve as a reliable measure of cognitive issues. The rise of consumer-grade eye tracking technology, which uses sophisticated imaging techniques in the visible light spectrum to determine gaze position, is noteworthy. In our study, we explored the feasibility of using webcam-based eye tracking to monitor the vergence eye movements of patients with Mild Cognitive Impairment (MCI) during a visual oddball paradigm. We simultaneously recorded eye positions using a remote infrared-based pupil eye tracker. Both tracking methods demonstrated more pronounced cognitive vergence responses for targets as opposed to distractors. In summary, the use of a consumer-grade webcam to record cognitive vergence shows potential. This method could potentially lay the groundwork for future research aimed at creating an affordable screening tool for mental health care.

Volunteer scuba divers have become indispensable stakeholders in marine environmental protection, as their educational knowledge and environmental awareness create environmentally responsible attitudes and behaviors, which are the key elements to promote the sustainable development of marine environments. This study used the C-A-B model to examine the relationship between environmental knowledge, environmental sensitivity, destination social responsibility, and environmentally responsible behaviors of volunteer scuba divers. It also explored the mediating effects of destination social responsibility and first-time and repeat volunteers. In order to achieve the research purpose, this study adopted quantitative research to verify the research structure, proposed the hypotheses and prepared a questionnaire with reference to relevant previous studies. A total of 238 Taiwan marine volunteer scuba divers were selected as the subjects by intentional sampling, and the effective questionnaire recovery rate is 92.9%. Structural equation was used to verify the model of environmentally responsible behaviors of volunteer scuba divers, as based on the C-A-B model. This study found that environmental knowledge had a significant impact on destination social responsibility, and destination social responsibility had a significant impact on environmentally responsible behaviors. Destination social responsibility also had the effect of complete mediation on the model. In addition, the first-time and repeat volunteers had a significant mediating effect on the causal relationship between environmental knowledge and environmentally responsible behaviors, as well as between environmental sensitivity and environmentally responsible behaviors. The explanatory power of the overall model on the environmentally responsible behaviors of volunteer scuba divers is 69.0%, and their knowledge, sense of responsibility, experience, and level of involvement can be regarded as the determinants of their environmentally responsible behaviors.

Brewers’ spent grain (BSG) is an important waste produced in beer companies with a high po-tential to be transformed into commercial by-products. The present paper reports a forecasting technology study with the aid of bibliographic review and patent analysis tools aiming to in-vestigate the maturity of certain technologies, considering the use of BSG on a closed-loop bio-refinery and circular bioeconomy concept integrated into a brewery. To evaluate the possibili-ties of production of high-added value products in Brazil, it was presented an overview of BSG’s scientific, technological, and marketing products and applications. The comparison between numbers of articles versus patents shows that the solutions proposed by research articles are not being transformed into maturated viable technologies. The results suggest that there is a gap between the scientific research in the institutions and their applications in industry, which lead to the destination of the BSG for more economically attractive investments when compared to research in Brazil. Ultimately, from the carried-out analysis, it was possible to propose a brew-ing process connected to the biorefinery system, showing the possibility to the newly arising brewery industries.

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

Triple-negative breast cancer (TNBC) is an aggressive and highly metastatic type of tumor. TNBC is often enriched in tumor-infiltrating neutrophils (TINs), which support cancer growth in part by counteracting tumor-infiltrating lymphocytes (TILs). Prior studies identified the enhancer of zeste homolog 2 (EZH2) as a pro-tumor methyltransferase in primary and metastatic TNBC. We hypothesized that EZH2 inhibition in TNBC cells per se would exert anti-tumor activity by altering the tumor immune microenvironment. To test this hypothesis, we used CRISPR to generate EZH2-knockout (KO) and overexpressing lines from parent (wild-type -WT) 4T1 cells, an established murine TNBC model. In vitro, EZH2 KO and OE cells showed little change in replicative capacity and invasiveness, but marked changes in surface marker profile and cytokine/chemokine secretion compared to WT cells. On in vivo injection, EZH2 KO cells showed significantly reduced primary tumor growth and a 10-fold decrease in lung metastasis compared to WT cells, while EZH2 OE cells were unchanged. Compared to WT tumors, TIN:TIL ratios were greatly reduced in EZH2 KO tumors but unchanged in EZH2 OE tumors. Our findings show a tumor-intrinsic role of EZH2 in the 4T1 TNBC model in regulating TIN/TIL poise, primary tumor progression and lung metastasis in vivo.

The goal was to model the irrigated (IBY) and rainfed (RBY) bean yields, as a function of essential climatic variables (ECVs), in the center (Culiacán) and south (Rosario) from Sinaloa. In Sinaloa, and for the period 1982–2013 (October–March), the following were calculated: a) temperatures. b) average degree days for the bean, c) cumulative potential evapotranspiration and d) cumulative effective precipitation. For ECVs, from the European Space Agency, e) daily soil moisture. f) IBY and RBY, from the Agrifood and Fisheries Information Service. Multiple linear regressions were applied, which modeled IBY–RBY (dependent variables), as a function of ECVs (independent variables). Then, to establish each Pearson correlation (PC) as significantly different from zero, a hypothesis test was applied: PC vs Pearson's critical correlation (CPC). The four models obtained were significantly predictive: IBY–Culiacán (PC = 0.590 > CPC = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|) and RBY–Rosario (PC = 0.532 > CPC = |0.349|). This study is the first in Sinaloa to predict IBY and RBY based on ECVs, contributing to the production of sustainable food.

The characteristics of the Sentinel-2 mission with a decametric resolution and frequent acquisitions allow to improve the identification of crops. The majority of the studies on crop classification using RS were targeted at herbaceous and gramineous crop classes while fewer results were obtained on woody crops which present a strong variability in management practices that make their identification difficult. Thus, this study aimed to propose a rapid, accurate, and cost-effective analytical approach for the delineation of fruit orchards (OC), vineyards (VY), and olive groves (OL) in the Mediterranean (Southern France) considering two locations. A classification based on phenology metrics (PM) de-rived from temporal Sentinel-2 time series was developed to perform the classification. The PM were computed by fitting a double logistic model on temporal profiles of vegeta-tion indices to delineate OC, VY, and a DC class gathering all remaining surfaces. The generated PM were introduced in a random forest (RF) algorithm to identify woody crops across the two sites. The method was tested on different vegetation indices, the best results being obtained with the leaf area index (LAI). To delineate OL in the DC class, the tem-poral features of the green chlorophyll vegetation index (GCVI) were found to be the most appropriated with a typical drop of the signal during the mid-season (DOY 150-250). As a final result, we obtained an overall accuracy ranging from 89-96% and Kappa of 0.86-0.95 by considering each study site and year (2016-2021), separately. This accuracy is much better than applying the RF algorithm on the LAI times series, which led to a Kappa rang-ing between 0.3 and 0.52 and demonstrates the interest of using phenological traits rather than the raw time series of the RS data. The method can be well reproduced from one year to another. Moreover, it is possible to apply the classification model of a given year to an-other, keeping good accuracy. This is an interesting feature to reduce the burden of col-lecting ground truth information. On the contrary, the use of a classification model cali-brated in one site and applied to another led to a strong degradation of the classification accuracy. Woody crop phenology is dependent on site climatic conditions as well as the cultivar and management practices that can differ from one site to another.

The purpose of this study was to investigate the effects of cognitive dual-tasking on gait initiation and sit-to-walk. Twenty-eight healthy young adults performed gait initiation and sit-to-walk under two conditions: while engaging in serial subtraction (dual-task) and without any additional task (single-task). Motion data were collected using a 10-camera optoelectronic motion capture system, synchronized with force plates and surface electromyography. We analyzed spatiotemporal parameters, center of mass displacement, center of pressure trajectory, and lower limb muscle activation patterns. We found that dual-task conditions significantly affected both gait initiation and sit-to-walk patterns, increasing the duration of transitions and mediolateral center of mass displacements, while reducing vertical center of mass displacements and forward propulsion. We also observed a more constrained and less efficient center of pressure path, with reduced posterior displacement during the weight shift phase. Muscle activation, particularly in the tibialis anterior and biceps femoris, decreased during dual-task conditions, indicating altered neuromuscular strategies. These findings suggest a shift in postural control demands and motor performance during dual-task transitions.

Due to the deteriorating state of surface waters and the resulting reduction in their availability for the population, efforts have been undertaken to develop a biodegradable or compostable water filtration system capable of reducing the amount of harmful bacteria in this environment. The aim of this research was to develop nonwovens using the classic needle-punching method with commercially available polylactide (PLA) polymer, and subsequently, to select appropriate concentrations of modifiers to create systems with antibacterial properties. The resulting composites could potentially serve as filtration materials for the purification of surface waters. Nanoparticles such as zinc oxide, titanium dioxide, and silver were employed in this research. These compounds exhibit antibacterial properties and are insoluble in water. To confirm the bactericidal activity of the additives, biological tests were conducted, and the distribution of these additives on the structure of the filtration material was examined using scanning electron microscopy (SEM) equipped with an energy dispersive spectroscopy (EDS) detector.

Melanin is extensively distributed in organisms and believed to be synthetized by tyrosinase (TYR), leading to aberrant coloring and skin conditions when over-produced. Loquat (Eriobotrya japonica (Thunb.) Lindl.) flowers contain large amounts of bioactive substances currently few studied on their suppressive capabilities to melanin synthesis. Its isolate product (LFP) was obtained by ethanol solution extraction and resin purification, of which the inhibitory mechanism on TYR activity was elaborated by enzyme kinetics and multi-spectroscopy determinations and the impact on related proteins’ expression in mouse melanoma B16 cells was analyzed with Western blotting. HPLC-MS analysis indicated that LFP was identified into 137 compounds, with 12 including flavonoids (quercetin, isorhamnoin, p-coumaric acid, etc.), cinnamic acid and its derivatives as well as benzene and its derivatives owning TYR inhibitory activities. LFP inhibited TYR activity in a concentration-dependent manner with its IC50 value being 2.8 mg/mL and an anti-competitive mechanism involved altering the enzyme's conformation. LFP reduced the expression of TYR, TRP1, and TRP2 in melanoma B16 cells, hence inhibiting the synthesis of melanin. Considered collectively, LFP would be a viable option for treating hyperpigmentation caused by tyrosinase, providing a theoretical basis for the value-added utilization of loquat flower resource in beauty and aging related functional products.

In data-scarce regions, prudent planning and precise decision-making for sustainable development especially in agriculture remains a challenging task due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use land cover (LULC) classes and crop identification. Applying Remote Sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/ algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resources management. The (this)study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/ classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The MODIS (Moderate Resolution Imaging Spectroradiometer) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with unsupervised classification technique to get LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as a key input for agricultural mechanization planning and resources management. The accuracy of the LULC map was 78%, assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced techniques of processing, which will make it more useful and applicable for regional agriculture and environmental management.

Time can be a difficult concept to understand. Albert Einstein, who viewed time as an illusion, explained the passage of time is relative to the observer’s frame of reference in the space-time continuum. However, the state of consciousness of the observer also influences the passage of time. This paper examines the relationship between the experience of time and different states of consciousness including near-death experiences, mystical states of consciousness, and psychedelic medicine-induced states of consciousness. Two different experiences of time are described. These are Chronos and Kairos. Chronos is described as 4th dimensional time whereas Kairos is 5th dimension time. Methods for facilitating the experience of Kairos are discussed and the potential benefits of experiencing Kairos are reviewed.

This paper explores the utilization of ensemble models and learning analytics techniques to predict student academic performance. With the advent of educational big data, institutions are increasingly leveraging advanced analytics to gain insights into student learning patterns and optimize educational outcomes. Ensemble models, which combine the predictive power of multiple algorithms, offer a robust approach to enhance prediction accuracy. The performance of the ensemble models was analyzed and compared using the Open University Learning Analytics Dataset, which consists of sources such as demographic information, historical performance data, and engagement metrics for 23,344 students. The evaluation of various ensemble models across different classification scenarios revealed that the proposed stacking model consistently emerges as the best-performing model, excelling in both multi-class and binary classification tasks.

The aim of this study was to identify factors, that influence satisfaction and dissatisfaction in the use of public e-procurement. The use of IT solutions promotes sustainable development. Hence, there is a need to conduct research that will allow for the best possible adjustment of e-procurement solutions in order to implement them as widely as possible in business practice. This study used a quantitative exploratory survey-based approach. The obtained research results were analyzed using statistical methods, including factor analysis PCA. Factor analysis identified three main satisfaction factors and two dissatisfaction factors in e-procurement use, highlighting the importance of support, intuitiveness, and security in user satisfaction. This study contributes to the existing literature by providing a nuanced understanding of the factors influencing user satisfaction and dissatisfaction within e-procurement systems, specifically highlighting the role of intuitive design and technical support. It offers a unique perspective by integrating these insights with the specific requirements of public procurement, thereby filling a gap in research on enhancing user experience in the context of sustainable purchasing practices.

Over the last fifty years, management plans have become more descriptive with regard to potential sustainability of forest systems, raising questions about the feasibility of implementing management activities and ensuring sustainability of a wide variety of ecosystem services. To assess this issue, we conducted a survey among forest planning and operation research communities to understand their perceptions regarding the potential of a sample of sustainability statements currently used in forest plans to be incorporated into optimization models or other mathematical operations. The results revealed that only a few statements from the sample were deemed to have relatively mature or firm methodologies and data to enable inclusion in modern mathematical models for land use optimization. These statements were mostly related to economic sustainability, offering quantifiable information such as a non-declining flow of wood products over time and limits on the amount of timber harvested per decade. In contrast, sociocultural and, to some extent, ecological statements regarding sustainability were generally perceived to be more difficult to translate into mathematical modeling efforts. Particularly challenging were statements corresponding to sustaining natural or scenic characteristics of a forest. These findings may be attributed to various factors, including a lack of measurable indicators for sustainability and a potential lack of understanding about the modeling components and their interactions with planned management activities.

Critical realism emerged from the philosophical writings of Roy Bhaskar, and has evolved into a philosophy of social science research using the model of “dialectical critical realism” (DCR) which begins with the researcher’s assumptions that the structures being researched have a real, ontological grounding which is independent of the researcher. This approach has proved fruitful in British and European social science research, but has had less influence in North America. We outline DCR’s four level model for understanding society and its changing social structures through “the pulse of freedom”. DCR has been used by Marxists, Muslims, Catholics and secular scholars who engage fruitfully in morphogenic dialogues leading to a critical realist understanding of society and social research, which transcends positivist and social constructionist models. Examples of DCR’s application in the fields of childhood research, child abuse, education, and research on organisations are outlined to illustrate the working of this new research paradigm. We are enthusiastic in our advocacy of DCR as a model of qualitative research, and for constructing models of positive social change, and are particularly impressed by the substantive and theoretical expositions of DCR by Priscilla Anderson, Matthew Wilkinson and Margaret Archer, whose work we document and review.

The application of the latest technological innovations has been promoted worldwide to increase farm productivity, including in salt farming. This research aims to determine the determinants of adoption decisions for salt production technology called geomembrane and estimate the adoption impact on technical efficiency. The data in this study is cross-sectional from 215 small-scale salt farmers on Madura Island, East Java, Indonesia. The data was analyzed using logistic regression to identify which factors influenced farmers’ decisions to use geomembrane. The influence of adoption on farmers' technical efficiency was then assessed using propensity score matching (PSM) and data envelopment analysis (DEA). The findings indicate that age and the dummy variables of gender, land ownership, profit-sharing involvement, and membership in the People's Salt Business Group (KUGAR) all had a significant impact on adoption rates. The findings of controlling matched samples using the PSM process reveal that geomembrane application improves and greatly increases farmers' technical efficiency. Those who used geomembranes displayed greater technical efficiency than those who did not. These findings imply that salt production technology should be promoted more to increase productivity, especially geomembrane adoption, through outreach and dissemination of information, including for landowners involved in the profit-sharing system.

BAT-based regulations necessitate defining Key Environmental Issues (KEIs) to focus data collection and discussions on the main topics when establishing Best Available Techniques (BATs) at the sector level. However, Article 14 of the Industrial Emissions Directive (IED) suggests that some significant environmental issues may not be covered by BAT conclusions but still require the implementation of BATs at the local level, even in the absence of defined sectoral BAT references. The IED, along with the associated Commission Implementing Decisions and guides, does not offer guidelines for selecting KEIs at the local level, whether by the competent authority or the operator. To ensure full compliance with the IED by installations under its scope, this paper proposes a methodology for determining KEIs locally. Based on the environmental aspects of the installation, the sensitivity of the environment, and the levels of emissions or consumption, this methodology has been tested on a case study at the plant level to demonstrate its effectiveness. The paper then discusses the contributions and limitations of the methodology and suggests areas for future research.

Interactive conversational agents, also known as chatbots, have the potential to increase the success rate of digital technology interventions to promote healthy behaviors. However, due to their newness and limited use, little is known about their integration, usefulness, and effectiveness in promoting smoking and vaping cessation. The aim of this mixed methods systematic review was to assess the effectiveness and characteristics of current interactive conversational agents in promoting and supporting smoking and vaping cessation. A mixed methods systematic review was conducted to identify studies published in the last 20 years in five relevant databases. Eight studies, including seven on smoking cessation and one on smoking and vaping cessation, were included. The results showed that, compared to other smoking cessation methods, chatbots can lead to better engagement in treatment, resulting in higher rates of sustained abstinence and improved quality of life. In addition, chatbots can be perceived as empathetic and establish a decent therapeutic alliance thanks to their communication skills. This knowledge could be useful for the development of interactive conversational agents to support smoking and/or vaping cessation. Alternative intervention tools targeting younger generations, such as chatbots, may offer an additional way for public health professionals to reach them.

The quality of wine depends upon the quality of the grapes, which, in turn, are affected by different viticulture aspects and the climate during the grape-growing season. Obtaining wine professionals’ judgments of the intrinsic qualities of selected wine products is a time-consuming task. It is also expensive. Instead of waiting for the wine to be produced, it is better to have an idea of the quality before harvesting, so that wine growers and wine manufacturers can use high-quality grapes. The main aim of the present study was to investigate the use of machine learning aspects in predicting wine quality and to develop a pipeline which represents the major steps from vineyards to wine quality indices. This pipeline outputs the predicted yield, values for basic parameters of grape juice composition, values for basic parameters of the wine composition, and quality. We also found that the yield could be predicted because of input data related to the characteristics of the vineyards. Finally, through the creation of a web-based application, we have investigated the balance berry yield and wine quality. Using these tools further developed, vineyard owners should be able to predict the quality of the wine they intend to produce from their vineyards before the grapes are even harvested.

Bio-char is a charcoal-like substance made through the pyrolysis of organic biomass. It has the potential to address issues related to agriculture and the environment. This thorough analysis examines the potentials of bio-char in Ethiopia to enhance crop productivity, reduce climate change, and improve soil fertility. Drawing from academic databases and peer-reviewed literatures, the review explores the production and characterization of bio-char, its impact on soil fertility and crop productivity, and its role in carbon sequestration and greenhouse gas mitigation. The review emphasizes on how bio-char may improve plant nutrition and soil fertility, which raises agricultural yields and quality while improving drought resistance. The review also emphasizes the potential of bio-char for greenhouse gas mitigation and carbon sequestration, enhancing Ethiopia's efforts to mitigate climate change. Yet the study also points out several challenges and opportunities that need to be addressed to allow Ethiopians to use bio-char technology on a large scale. These include the need for appropriate policy and regulatory frameworks, the availability and accessibility of bio-char feedstocks, and the importance of continued research and development to optimize production methods and application strategies. The review concludes by highlighting the groundbreaking potential of bio-char in promoting environmentally friendly agricultural practices and sustainable agriculture in Ethiopia. It necessitates a coordinated effort to solve the problems and grasp the opportunities by making sound decisions in policy integration, research, and development. Ethiopia can advance its sustainable development goals by using bio-char to create a road towards increased food security, healthy soil, and environmental resilience.

Many children who live in the interior and mountainous areas of Papua-Indonesia do not get the right of education. This research aims to formulate a theoretical concept of boarding school-based character education by implementing Papuan cultural contextual education in the process of education. The method used is a qualitative method with a grounded theory approach. The research participants were 14 people. Data were collected through semi-structured interviews and textual analysis of the interview results. The data analysis technique uses the Atlas.ti application by using three levels of coding: open coding, axial coding, and selective coding. The research results show that there are six main concepts as a model of boarding school character education: personal development, spiritual development, extracurricular activities, clean and healthy lifestyles, learning from inspirational figures, and academic development. These six theoretical concepts can be divided into internal and external character building. One of the uniqueness of character education is the integration of local cultural values in the whole process of character education of the children, both in the school environment and in the dormitory. The results of the study provide insights for educators, schools, and government on different forms of character education models.

The Aether Physics Model (APM) presents a radical reinterpretation of fundamental physics, offering potential solutions to long-standing problems in Quantum Mechanics and General Relativity. This paper explores the core concepts and equations of the APM, demonstrating how they provide a unified framework for understanding the nature of space, time, matter, and fundamental forces. We examine the implications of the APM's quantized Aether structure, the role of the Singularity and Gforce, and the reinterpretation of fundamental constants and forces. The model's predictions regarding dark matter, neutron content in stable matter, and the unification of forces are discussed, along with potential experimental tests. Additionally, we introduce the loxodrome concept and toroidal particle topology as central elements in explaining fundamental particles and forces. While speculative, the APM offers a thought-provoking alternative to standard physics models and warrants further investigation.

Classifiers face a myriad of challenges in today’s data-driven world, ranging from overfitting and high computational costs to low accuracy, imbalanced training datasets, and the notorious black box effect. Furthermore, many traditional classifiers struggle with the robust handling of noisy and missing feature values. In response to these hurdles, we present classification methods that leverage the power of feature partitioning learning and outranking measures. Our classification algorithms offer an innovative approach, eliminating the need for prior domain knowledge by automatically discerning feature intervals directly from the data. These intervals capture essential patterns and characteristics within the dataset, empowering our classifiers with newfound adaptability and insight. In addition, we employ outranking measures to mitigate the influence of noise and uncertainty in the data. Through pairwise comparisons of alternatives on each feature, we enhance the robustness and reliability of our classification outcomes. The developed classifiers are empirically evaluated on several data sets from UCI repository and are compared with well-known classifiers including k Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), Naive Bayesian (NB) and Nearest Centroid (NC). The experiments result demonstrate that the classifiers based on feature interval learning and outranking approaches are robust to imbalanced data and to irrelevant features and achieve comparably and even better performances than the well-known classifiers in some cases. Moreover, our proposed classifiers produce more explainable models whilst preserving high predictive performance levels.

For college students, speaking skills are a priceless commodity that can solve a wide range of social, professional, and academic problems. Proficiency in oral communication allows students to express themselves coherently and convincingly in academic and workplace contexts, which improves their involvement in group projects, presentations, and class debates. This paper determines to collect literature to prove that speaking skills can be a panacea for college students in the classroom and the workplace. Having speaking skills is, in fact, a college student's greatest asset, providing a plethora of advantages that go beyond scholastic achievement to encompass personal and professional growth. These abilities should be prioritized in colleges and institutions, with thorough curricula incorporating technological integration, practice opportunities, and public speaking courses. More investigation is required to look into creative approaches to improving speaking abilities and helping students get past obstacles. In essence, emerging strong speaking skills prepare college students with a multipurpose device that improves various aspects of their lives, making it a true panacea.

This study investigates how students' well-being relates to sustainable development and education for sustainability at Prince Mohammad Bin Fahd University, Saudi Arabia. The authors endeavor to answer the question "How Does Students' Well-Being Relate to Sustainable Development Via Education for Sustainable Development?" and four sub-questions. Primary data were collected by semi-structured interviews exploring the viewpoints of twenty female and male students (n=26) aged between 18 and 26. The content analysis results have identified additional well-being and education for sustainability factors that influence sustainable education and, therefore, sustainable development. These antecedents include workload, transparency, grading criteria, fairness, belonging, work-life balance, mental health support, missing emotional intelligence factors, factors related to Education for Sustainable Development, such as instructors' role models, institutional commitment, leadership, and collaboration with government. In addition, the study developed a theoretical model for the relationship between antecedents for Education for Sustainable Development, Education for Sustainable Development, and Sustainable Development in the Saudi context. Finally, the study answers four research questions: What factors contribute to education for sustainable development? What are the antecedents of education for sustainable development? How do university educators contribute to sustainable education? How do the antecedents of education for sustainable development, education for sustainable development, and sustainable development relate?

Nanotechnology sparks discussions and concerns about the impacts of new nanomaterials on health and the environment. It holds importance in various domains due to its unique struc-tures. Carbon dots (C-dots) are versatile nanomaterials with applications in bioimaging, sensing, catalysis, polymers, solar cells, and more. They offer desirable characteristics such as stability, cost-effectiveness, biocompatibility, and high photoluminescent quantum yield. C-dots have the potential to replace expensive fluorophores in solar cells. They are catego-rized as carbon quantum dots, graphene quantum dots, and carbonized polymer dots. C-dots have outstanding optical and photoelectric properties with low toxicity. Bottom-up and top-down approaches are used for the synthesis of carbon dots (CDs), with each method im-pacting their physicochemical characteristics. The choice of synthesis method depends on de-sired properties and application requirements. Researchers combine these methods and ex-plore new approaches to enhance synthesis efficiency and tailor CD properties. CDs have di-verse chemical structures with modified oxygen, polymer-based, or amino groups on their surface. Various characterization methods such as HRTEM, XPS, and optical analysis (PL, UV) are used to determine CD structure. Carbon dots (CDs) are cutting-edge fluorescent nano-materials with remarkable qualities such as biocompatibility, low toxicity, environmental friendliness, high water solubility, and photo-stability. They are easily adjustable in terms of their optical properties, making them highly versatile in various fields. CDs find applications in bio-imaging, nanomedicine, drug delivery, solar cells, LEDs, photo-catalysis, elec-tro-catalysis, and other related areas.

The opportunities and advantages of implementing Artificial Intelligence (AI) and Machine Learning (ML) to optimize the process control of wastewater treatment in the Oil and Gas industry are discussed in this work. Discussion is centered around five major points: 1) Various types of Oil and Gas wastewater (Sour, Oily, Produced) and industry standards/methods for treatment, including specific case studies and challenges when treating certain wastewater. This section aims at the common types of wastewater in the Oil and Gas industry and how they are generally treated. 2) How AI/ML has been implemented in wastewater treatment in various industries; summary of which AI/ML languages were used along with a brief overview of how they work. This article focused on an understanding of the specific case studies and AI explanations to understand the effectiveness of wastewater treatment using AI/ML. 3) AI that is currently implemented in Oil and Gas such as personal protective equipment (PPE), smartwatches radiofrequency identification etc. This showed that AI/ML already plays a successful role in the Oil and Gas industry and has proven to help optimize many processes and components. Many of these AI/ML implementations are now industry standards and have greatly benefitted the industry. This section demonstrates that the implementation and the use of AI/ML have been proven to be a successful endeavor and that expanding the role of AI/ML would be beneficial. 5) Using the established points from the previous sections the paper outlines why AI/ML is ideal for Oil and Gas wastewater treatment; this argument centers around fit-for-purpose treatment, and the possible uses of the treated water. A determination of the best-suited AI languages, possible implementation scenarios, and how this would shift the way Oil and Gas industries treat and approach wastewater is presented to the reader.

Background: The incidence of spinal metastatic disease has risen with improved cancer survivorship. Separation surgery, the current standard surgical treatment, does not fully address spinal deformity. Advances in operative management warrant reevaluating maximal tumor resection and anterior column reconstruction with corpectomy. Methods: An IRB-approved retrospective single-center review of 63 patients was performed. Demographics, tumor characteristics, clinical and radiographic data, treatments, and neurological and survival outcomes were analyzed. Results: The mean age at presentation was 63.5 ± 9.6 years (range, 37-84 years) and 28 patients (44.4%) were female. Functional outcomes, measured by KPS, RAND-36 survey scores and Oswestry Disability Index all improved postoperatively (pre- vs postop: 70 to 80 (p=0.0056), 26.5 to 46.4 (p=0.0081), and 28.9 to 16.9 (p=0.0613), respectively). Spinal alignment also improved, with Cobb angle measurements showing a reduction in spinal kyphosis from -5 to 0 (p=0.021). Nine patients (14.5%) had non-fatal postoperative complications. The median OS for this cohort was 14.4 months (range, 0.6-145.0 months) with median follow-up time of 15.5 months. Conclusion: In oligometastatic spinal disease patients with good functional status, corpectomy for decompression of neural elements, deformity correction, and spinal stabilization is associated with improvements in pain and functional outcomes without overwhelming morbidity.

Background: Musculoskeletal pain is a non-negligible multifaceted condition affecting more than 30% of the global population. Superficial heat therapy (HT) through increasing tissues temperatures plays a role in increasing local metabolism and function and relieving pain. Knee pain and sports represent two relevant fields of superficial HT application. Methods: In the present paper a panel of experts performed a narrative review of the literature regarding the role of superficial HT in the management of knee pain and sport activities. Results: According to the reviewed literature, HT represents a therapeutic option in the management of musculoskeletal pain due to three main effects: pain relief, promotion of healing and return to normal function and activity. Moreover, HT plays a role in sport activities both before and after exercise. Before performing sports, HT helps in preparing muscles for performance. After performing sports, it is capable to promote recovery and healing pathways. Combining and sequencing superficial heat and cold therapy represent an interesting topic of study. Overall, application of heat wraps for superficial HT can be considered safe. Conclusions: Despite being an ancient method, HT shows to be a potentially beneficial and safe option in the management of several conditions including KP and sports. The key in the application of superficial HT is a multimodal and multidisciplinary approach.

The growing global interest in Circular Economy (CE) adoption by organizations has received considerable attention in academic literature. This review delves into the multifaceted aspects of CE transformation, assessing barriers, drivers, and potential research directions. It encompasses three main clusters: frameworks and definitions, barriers and drivers, and industry-specific research. Existing studies significantly contribute by unveiling strategies, frameworks, and innovative ap-proaches aiding CE transition. However, there's a notable focus on identifying barriers hindering CE implementation, revealing a critical need for effective solutions. Moreover, the review highlights the lack of CE frameworks in the service sector and insufficient awareness among Small and Me-dium Enterprises, necessitating deeper exploration. Despite the potential of digital technologies like Industry 4.0 in expediting CE transformation, comprehensive implementation strategies remain lacking. Noteworthy frameworks, such as circular business models and two-stage transformation models, offer practical pathways for CE adoption. Dynamic capabilities emerge as a crucial yet underexplored factor facilitating CE transition. Future research should delve into country-specific contexts, integrate digital technologies into industries, and explore the relationship between dy-namic capabilities and CE transformation, especially within small and medium-sized enterprises. While progress has been made in understanding CE principles' adoption, this review highlights research gaps that require attention for global sustainability and widespread CE practice imple-mentation.

The evaluation of tourism sustainability has become of paramount importance for researchers and decision-makers to better protect tourist destinations, especially with the continuous increase in the number of tourists and visitors that alter the natural components of the environment and degrade ecosystems. Therefore, the use of geographical data is of great significance, particularly in environmental monitoring and land management, due to its ease of use and availability. However, calculating composite indices for sustainability involves several steps and requires a basic understanding of existing models. This article describes the development and design of a package of plugins in QGIS that serves as an easy-to-use tool for processing and automatically calculating economic, environmental, social indices, and the overall index of tourist sustainability. These plugins have been built in Python and designed as QGIS software plugins.

Cold atmospheric plasma (CAP) has emerged as a promising therapeutic technology, distinguished by its safety, efficiency, and minimal side effects. With increasing applications in dermatology, CAP demonstrates antimicrobial efficacy against bacteria, viruses, and fungi, supports tissue proliferation and wound healing, and hinders the growth and migration of tumor cells. This review encompasses the concept, physical characteristics, and current research applications of CAP in dermatology. CAP, a gas cloud produced by ionizing gases, operates at room temperature, ensuring safety and efficacy in dermatological use. Its effects are mediated through active components like reactive oxygen and nitrogen species (RONS) and ultraviolet radiation. The extensive research covered in this paper explores CAP's applications in skin infections, immune-related diseases, and tumor diseases. As a biologically suitable technology, cold atmospheric pressure plasma finds widespread use in medicine, including medical device sterilization, dentistry, oncology, and dermatology. The direct and indirect forms of CAP applications, including plasma-activated liquids, complement each other. CAP holds promise as a dermatological treatment technique, but further exploration and research are needed. This article briefly explores CAP's diverse applications and potential utility within dermatology. The objective is to enhance the progress of plasma medicine by furnishing a concise overview of the applications of this groundbreaking technology in dermatological contexts.

Digital Image Correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the DIC methodology has validated its effectiveness as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

A model to rationalize the effects of test temperature and microstructural variables on the creep crack growth (CCG) and creep-fatigue crack growth (CFCG) rates in ferritic steels is described. The model predicts that as the average spacing between grain boundary particles that initiate creep cavities decrease, the CCG and CFCG rates increase. Further, CCG data at several temperatures collapse into a single trend when a temperature compensated CCG rate derived from the model is used. CCG and CFCG behavior measured at different temperatures is used to assess the effects of variables such as differences between basemetal (BM), weldmetal (WM) and the heat-affected zone (HAZ) regions. The model is demonstrated for Grade 22 and Grade 91 steels using data from literature. It is shown that differences between the CCG behavior of Grade 22 steel in new and ex-service conditions are negligible in the BM and WM regions but not in the HAZ region. The CCG behavior of Grade 91 steels can be separated into creep-ductile and creep-brittle regions. The creep-brittle tendency is linked to the presence of excess trace element concentrations in the material chemistry. Significant differences found in the CCG rates between BM and the WM and HAZ regions of Grade 91 steel were explained.

Background: The heterogeneity of rectal biopsy techniques encourages us to search for a surgical and pathological standardisation of this diagnostic technique to exclude Hirschsprung’s disease. The varying amount of information on the anatomopathology report prompts us to compile a template concerning the anatomopathology report for diagnostic rectal biopsies for colleague surgeons and pathologists working on Hirschsprung’s disease. Methods: We gathered the anonymous biopsy information and its pathology information from five hospitals of all patients in which rectal biopsies were taken to diagnose Hirschsprung’s disease over two years (2020-2021). Results: 82 biopsies: 20 suction (24,4%), 31 punch (37.8%) and 31 open biopsies (37,8%) were taken. Of all biopsies 69 were conclusive (84,2%), 13 were not (15,8%). In the suction biopsy group 60% were conclusive, 40% not, for punch biopsy 87% and 13% respectively and for open biopsy 97% and 3%. Inconclusive results were due to insufficient submucosa in 6/8 suction biopsies, 4/4 punch and 0/1 open biopsies. An insufficient amount of submucosa was the reason for an inconclusive result in 6/20 cases (30%) after suction biopsy, 4/31 (12,9%) after punch biopsy and 0 cases (0%) after open biopsy. We had one case with major postoperative bleeding post suction biopsy; there were no further adverse effects after biopsy. Conclusions: Diagnostic rectal biopsies in children are safe. Non-surgical biopsies are more likely to give inconclusive results, due to less submucosa present in the specimen. Open biopsies are especially useful when previous non-surgical biopsies are inconclusive. An experienced pathologist is a key factor for the result. The anatomopathology report should specify the different layers present in the specimen, the presence of ganglion cells and hypertrophic nerve fibers, their description and a conclusion.

Important first phases in the process of implementing CO2 subsurface and ocean storage projects include selecting of best possible location(s) for CO2 storage, site selection evaluation. Sites must fulfil a number of criteria that boil down to the following basics: they must be able to accept the desired volume of CO2 at the rate at which it is supplied from the CO2 source(s); they must as well be safe and reliable; and must comply with regulatory and other societal requirements. They also must have at least public acceptance and be based on sound financial analysis. Site geology; hydrogeological, pressure, and geothermal regimes; land features; location, climate, and access, etc. can all be refined from these basic criteria. In addition to aiding in site selection, site characterization is essential for other purposes, such as foreseeing the fate and impacts of the injected CO2, and informing subsequent phases of site development, including design, permitting, operation, monitoring, and eventual abandonment. According to data from the IEA, in 2022, emissions from Africa and Asia's emerging markets and developing economies, excluding China's, increased by 4.2%, which is equivalent to 206 million tonnes of CO2 and were higher than those from developed economies. Coal-fired power generation was responsible for more than half of the rise in emissions that were recorded in the region. The difficulty of achieving sustainable socio-economic progress in the developing countries is entwined with the work of reducing CO2 emissions, which is a demanding project for the economy. Organisations from developing countries, such as Bangladesh, Cameroon, India, and Nigeria, have formed partnerships with organisations in other countries for lessons learn and investment within the climate change arena. The basaltic rocks, coal seams, depleted oil and gas reservoirs, soils, deep saline aquifers, and sedimentary basins that developing countries (Bangladesh, Cameroon, India, and Nigeria etc) possesses all contribute to the individual country's significant geological sequestration potential. There are limited or no carbon capture and storage or clean development mechanism projects running in these countries at this time. The site selection and characterization procedure are not complete without an estimate of the storage capacity of a storage location. Estimating storage capacity relies on volumetric estimates because a site must accept the planned volume of CO2 during the active injection period. As more and more applications make use of site characterization, so too does the body of written material on the topic. As the science of CO2 storage develops, regulatory requirements are implemented, field experience grows, and the economics of CO2 capture and storage improve, so too will site selection and characterisation change.

Waste-to-Hydrogen (Wahh) is a process of converting waste into hydrogen gas through various methods such as thermal conversion, biological conversion, and chemical conversion. This process not only helps in managing waste but also provides a clean source of energy as hydrogen is a renewable energy source that produces zero greenhouse gas emissions during combustion. Wahh has the potential to provide a sustainable solution for energy generation and waste management in the current scenario of globally increasing energy demands. This study aims to examine the properties and energy efficiencies of various renewable fuels including fuelwood, biogas, biomethane, and biohydrogen with a specific focus on differentiating the characteristics of biohydrogen. The paper explores the emerging concepts of waste-to-hydrogen microgrids, biohydrogen refineries, biohydrogen prosumer networks, and a biohydrogen circular economy, as well as Public-Private-People Partnerships (4Ps) and the hydrogen-centric renewable energy revolution, which are taking shape in industrialized countries. The study presents a comprehensive feasibility analysis of these concepts in the context of Nepal. The results of this research will provide valuable insights into the potential for adopting these innovative technologies in Nepal and will contribute to the development of a sustainable and efficient energy system.

Since Hydrogen Sulfide (H2S) was recognized as a gas transmitter, its detection and quantifica-tion have become a hot research topic among chemists and biologists. In this area. fluorescent probes have shown great advantages: fast and strong responds, low detection limit and easy ma-nipulation. Here we developed a new fluorescent probe that detected H2S selectively among various bioactive and inorganic salts. This probe was based on the core structure of fluorescein and reacted with H2S through azide-reduction. Great linearity was achieved correlating fluores-cence intensity and H2S concentrations in solution. The detection of H2S in cancer cells was also achieved.

Diabetes mellitus is a global health issue that necessitates the development of novel therapeutic approaches. Today numerous experimental methods have been used and set up to evaluate novel drugs with antidiabetic effects. In addition, several research studies have been done to induce diabetes under in vitro and in vivo conditions, but only a small number of these efforts are currently in a usable stage. This review aims to provide useful guidance for researchers who seek to evaluate the antidiabetic potential of drugs, agents, or new medications using the standard methods published by authorities and academics worldwide organizations and research centers. In this review, we collected 132 relevant articles from prominent databases such as PubMed, ScienceDirect, Scopus and others to summarize in vitro and in vivo experimental validation of antidiabetic treatment from 1943 to present.

This paper aims at computing feasible control strategies and the corresponding feasible state trajectories to drive an autonomous rear-axle bicycle robot from a given initial state to a final state such that the total running cost is minimized. Pontryagin’s Minimum Principle is applied and derives the optimality conditions from which the feasible control functions, expressed as functions of state and costate variables, are substituted into the combined state-costate system to obtain a new free-control state-costate nonlinear system of ordinary differential equations. A computer program was written in Scilab to solve the combined state-costate system and obtain the feasible state functions, the feasible costate functions and the feasible control functions. Associated Computational Simulations were provided to show the effectiveness and the reliability of the approach.

A critical aspect of coronary heart disease (CHD) care and secondary prevention is ensuring patients have access to evidence-based information. The purpose of this review is to summarise the guiding principles, content, context and timing of information and education that is beneficial for supporting people with CHD and potential communication strategies including digital interventions. We conducted a scoping review involving searching four databases (Web of Science, PubMed, CINAHL, Medline) for articles published from January 2000 to August 2022. Literature was identified through title and abstract screening by expert reviewers. Evidence was synthesised according to the review aims. Results demonstrated that information-sharing, decision-making, goal-setting, positivity and practicality are important aspects of secondary prevention and should be patient-centred and evidenced based with consideration of patient need and preference. Initiation and duration of education is highly variable between and within people, but hence communication and support should be regular and ongoing. In conclusion, text messaging programs, smartphone applications and wearable devices are examples of digital health strategies that facilitate education and support for patients with heart disease. There is no one size fits all approach that suits all patients at all stages and hence flexibility and a suite of resources and strategies is optimal.

Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm lead to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed internet of drones (IoD) in smart cities also brings up new concerns on performance, security, and privacy. The centralized framework adopted by conventional UAM networks is not adequate to handle high mobility and dynamicity. Moreover, it is necessary to ensure device authentication, data integrity, and privacy preservation in UAM networks. Thanks to characteristics of decentralization, traceability, and unalterability, Blockchain is recognized as a promising technology to enhance security and privacy for UAM networks. In this paper, we introduce LightMAN, a lightweight microchained fabric for data assurance and resilience-oriented UAM networks. LightMAN is tailored for small-scale permissioned UAV networks, in which a microchain acts as a lightweight distributed ledger for security guarantees. Thus, participants are enabled to authenticate drones and verify the genuineness of data that is sent to/from drones without relying on a third-party agency. In addition, a hybrid on-chain and off-chain storage strategy is adopted that not only improves performance (e.g,.latency and throughput) but also ensures privacy preservation for sensitive information in UAM networks. A proof-of-concept prototype is implemented and tested on a Micro Air Vehicle Link (MAVLink) simulator. The experimental evaluation validates the feasibility and effectiveness of the proposed LightMAN solution.

This paper aims at computing feasible control strategies and the corresponding feasible state trajectories to drive an autonomous rear-axle bicycle robot from a given initial state to a final state such that the total running cost is minimized. Pontryagin’s Minimum Principle is applied and derives the optimality conditions from which the feasible control functions, expressed as functions of state and costate variables, are substituted into the combined state-costate system to obtain a new free-control state-costate nonlinear system of ordinary differential equations. A computer program was written in Scilab to solve the combined state-costate system and obtain the feasible state functions, the feasible costate functions and the feasible control functions. Associated Computational Simulations were provided to show the effectiveness and the reliability of the approach.

In this paper a new monolithic Eulerian formulation in the framework of non-classical continuum is presented for the analysis of fluid-strucutre interaction problems. In this respect, Cosserat continuum theory taking into account the micro-rotational degrees of freedom of the particles is considered. Continuum description of the model and variational formulation of the governing flow dynamics for non-classical -fluid-structure interaction nCFSI is presented. The model is analyzed by computing a well known benchmark problem by Hecht and Pironneau [16]. The algorithmic description is presented and implemented using FreeFEM++. Code is validated with the benchmark solution of Turek and Hron [38] in case of flow around a flag attached with cylinder. New microstructral behavior of the solution is studied and numerical simulations and results are shown in the form of figures. Some interesting feature of the flow is observed and microstructural characteristics are discussed.

An updated, binary-coded message has been developed for transmission to extraterrestrial intelligences in the Milky Way galaxy. The proposed message includes basic mathematical and physical concepts to establish a universal means of communication followed by information on the biochemical composition of life on Earth, the Solar System’s time-stamped position in the Milky Way relative to known globular clusters, as well as digitized depictions of the Solar System, and Earth’s surface. The message concludes with digitized images of the human form, along with an invitation for any receiving intelligences to respond. Calculation of the optimal timing during a given calendar year is specified for potential future transmission from both the Five-hundred-meter Aperture Spherical radio Telescope in China and the SETI Institute’s Allen Telescope Array in northern California to a selected region of the Milky Way which has been proposed as the most likely for life to have developed. These powerful new beacons, the successors to the Arecibo radio telescope which transmitted the 1974 message upon which this expanded communication is in part based, can carry forward Arecibo’s legacy into the 21st century with this equally well-constructed communication from Earth’s technological civilization.

(1) Background: Patients with schizophrenia have higher mortality, with cardiovascular diseases being the first cause of mortality. This study aims to estimate the excess risk of hospital admission for cardiovascular events in schizophrenic patients, adjusting for comorbidity and risk factors. (2) Methods: The APNA study is a dynamic prospective cohort of all residents in Navarra, Spain. 505889 people over 18 years were followed for five years. The endpoint was hospital admissions for a cardiovascular event. Direct Acyclic Graphs (DAG) and Cox regression were used. (3) Results: Schizophrenic patients had a Hazard Ratio (HR) of 1.414 (95% CI 1.031-1.938) of hospital admission for a cardiovascular event after adjusting for age, sex, hypertension, type 2 diabetes, dyslipidemia, smoking, low income, obesity, antecedents of cardiovascular disease, and smoking. In non-adherent to antipsychotic treatment schizophrenia patients, the HR was 2.232 (95% CI 1.267-3.933). (4) Conclusions: Patients with schizophrenia have a higher risk of hospital admission for cardiovascular events than persons with the same risk factors without schizophrenia. Primary care nursing interventions should monitor these patients and reduce cardiovascular risk factors.

Development of the central nervous system (CNS) depends on accurate spatiotemporal control of signalling pathways and transcription programs. Forkhead Box G1 (FOXG1) is one of the master regulators that plays fundamental roles in forebrain development, from the timing of neurogenesis to the patterning of the cerebral cortex. Mutations in the FOXG1 gene cause a rare neurodevelopmental disorder called FOXG1 syndrome, also known as congenital form of Rett syndrome. Patients presenting with FOXG1 syndrome manifest a spectrum of phenotypes ranging from severe cognitive dysfunction and microcephaly to social withdrawal and communication deficits with varying severities. To develop and improve therapeutic interventions, there has been considerable progress towards unravelling the multi-faceted functions of FOXG1 in neurodevelopment and pathogenesis of FOXG1 syndrome. Moreover, recent advances in genome editing and stem cell technologies, as well as increased yield of information from high throughput omics opened promising and important new avenues in FOXG1 research. In this review, we provide a summary of clinical features and emerging molecular mechanisms underlying FOXG1 syndrome, and explore disease-modelling approaches in animals and human-based systems to highlight prospects of research and possible clinical interventions.

Since HIV was first identified, and in a relatively short period of time, AIDS has become one of the most devastating infectious diseases of the 21st century. Classical antiretroviral therapies were a major step forward in disease treatment options, significantly improving the survival rates of HIV-infected individuals. Even though these therapies have greatly improved HIV clinical outcomes, antiretrovirals (ARV) feature biopharmaceutic and pharmacokinetic problems such as poor aqueous solubility, short half-life and poor penetration into HIV reservoir sites, which contribute to the sub-optimal efficacy of these regimens. To overcome some of these issues, novel nanotechnology-based strategies for ARV delivery towards HIV viral reservoirs have been proposed. The current review focus on the benefits of using lipid-based nanocarriers for tuning the physicochemical properties of ARVs to overcome biological barriers upon administration. Furthermore, a correlation of these properties and the potential therapeutic outcomes has been established. Biotechnological advancements using lipid nanocarriers for RNA interference delivery for the treatment of HIV infections were also discussed.

The COVID-19 pandemic, like previous major crises, such as the 2008 financial crisis, has had a severe negative impact on international trade flows. International institutions are now exploring ways to help their member states recover from the health crisis, and foster the resilience of their economies to future crises. As far as trade is concerned, institutions that deal primarily with trade matters are making effort to help their member states foster the resilience of their trade performance to future shocks. In this context, the World Trade Organization (WTO), which is the only international organization that deals with the global rules of trade between nations, has organized a series of events since the onset of the COVID-19 pandemic. It has now planned to hold in September 2021 the 2021 WTO Public Forum whose theme is "Trade Beyond COVID-19: Building Resilience". The present paper aims to contribute to this debate by examining the effect of development aid, i.e., the so-called official development aid, in particular its Aid for Trade (AfT) component, on export resilience. The resilience of exports refers to the capacity of countries' aggregate exports to resist to shocks, whether environmental or external shocks. The core argument of the analysis is that development aid would affect export resilience through its effect on productive capacities. The analysis covers 93 developing countries over the period 2002-2018. The findings indicate that total development aid flows, including both AfT flows and NonAfT flows exert a positive effect on export resilience. Among AfT components, AfT for productive capacities appears to exert a higher positive effect on export resilience than AfT for economic infrastructure and AfT for trade policy and regulation. In addition, development aid (whatever the aid variable considered) exerts the highest positive effect on export resilience in countries (such as Least developed countries - LDCs) that have the lowest level of productive capacities. These findings highlight the need for donor-countries to supply higher development aid flows, in particular AfT flows to countries such as LDCs that have low levels of productive capacities.

During the night between 9 and 10 September 2017, multiple flash floods associated to a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours, associated with a return period higher than 200 years, caused all the largest streams of the Livorno municipality to flood several areas of the town. We used the limited-area Weather Research and Forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. By providing more accurate descriptions of the low-level flow and a better assessment of the atmospheric water vapour, the results demonstrate that assimilating radar data improved the quantitative precipitation forecasts.

In contrast to normal cells, tumor cells of multiple entities overexpress the Heat Shock Protein 70 (Hsp70) not only in the cytosol, but also present it on their plasma membrane in a tumor-specific manner. Furthermore, membrane-Hsp70 positive tumor cells actively release Hsp70 into lipid microvesicles termed exosomes into the blood. Due to conformational changes of Hsp70 in the lipid environment, most commercially available antibodies fail to detect membrane-bound and exosomal Hsp70. To fill this gap and to assess the role of exosomal Hsp70 in the circulation as a potential tumor biomarker, we established the novel complete Hsp70 (compHsp70) sandwich ELISA using two monoclonal antibodies (mAbs) that are able to recognize both, free and lipid-associated Hsp70 on the cell surface of viable tumor cells and exosomes. The epitopes of the mAbs cmHsp70.1 (aa 451-461) and cmHsp70.2 (aa 614-623) that are conserved among different species reside in the substrate-binding domain of Hsp70, with measured affinities of 0.42 nM and 0.44 nM, respectively. Validation of the compHsp70 ELISA revealed a high intra- and inter-assay precision, linearity in a concentration range of 1.56 to 25 ng/ml, high recovery rates of ‘spiked’ liposomal Hsp70 (>84%), comparable values between human serum and plasma samples, and no interference by food intake or age of the donors. Hsp70 concentrations in the circulation of patients with glioblastoma, squamous cell or adeno non-small cell lung carcinoma (NSCLC) at diagnosis were significantly higher than those of healthy volunteers. Hsp70 concentrations dropped concomitantly with the decrease in viable tumor mass on irradiation of patients with approximately 20 Gy (range 18 – 22.5 Gy) or after completion of radiotherapy (60 - 70 Gy). In summary, the compHsp70 ELISA presented herein provides a highly sensitive and reliable tool for measuring free and exosomal Hsp70 in liquid biopsies of tumor patients, levels of which can be used as a predictive tumor-specific biomarker, risk assessment and for monitoring therapeutic outcome.

Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to trigger maintenance before a possible breakdown; however, much less focus has been devoted to the use of such PM predictions as feedback in automated process control mechanisms. They usually integrate preventive solutions to protect the machines, usually causing downtimes. The premise of this study is to develop a holistic adaptive process scheduling mechanism that incorporates PM analysis as a safety component to optimize the operation mode of an industrial process toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. As PM is largely a data-driven approach; hence, relies on the setup, we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time adaptive process scheduling mechanism. It schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, according to the abnormal readings. To demonstrate the pipeline on action, we implement our approach on a small-scale conveyor belt system utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control provides an efficient process with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process.

Globally, there are 1.35 million road fatalities every year, which are estimated to cost governments approximately US$ 518 billion, making road fatalities the 8th leading cause of death across all age groups and the leading cause of death of children and young adults. In South Africa, despite tremendous governmental efforts to curb the soaring trajectory of road accidents, the annual number of road fatalities has increased by 26% in recent years. By fitting a structural equation model (SEM) and a GARCH Model (Generalized Auto-Regressive Conditional Heteroskedastic-ity) to analyze and predict future trend of road accidents (number of road accidents, number of casualties, number of fatal crashes and number of persons killed) in South Africa, we propose and test a complex metamodel that integrates multiple causality relationships. We show an increasing trend of road accidents over time, a trend that is predictable by number of vehicles in the country, the population of the country and the total distance travelled by vehicles. We further show that death rate linked to road accidents is on average 23.14 deaths per 100,000 persons and is pre-dictable following the equation: y = -0.0114x2+1.2378x-2.2627 (R2=0.76) with y = death rate and x = year. Finally, in the next decade, the number of road accidents is predicted to be roughly constant at 617,253 accidents but can reach 1 896 667 accidents in the worst-case scenario. The number of casualties was also predicted to be roughly constant at 93 531 over time, although this number may reach 661 531 in the worst-case scenario. However, although the number of fatal crashes may decrease in the next decade, it is forecasted to reach 11 241 within the next 10 years with the worse scenario estimated at 19 034 within the same period. At the same time, the number of persons killed in fatal crashes is also predicted to be roughly constant at 14 739 but may also reach 172 784 in the worse scenario. Overall, the present study reveals perhaps the positive effects of government initiatives to curb road accidents and their consequences; we call for more stronger actions for a drastic reduction in road accident events in South Africa.

We consider the problem of scheduling $n$ jobs with identical processing times and given release as well as delivery times on $m$ uniform machines. The goal is to minimize the makespan, i.e., the maximum full completion time of any job. This problem is well-known to have an open complexity status even if the number of jobs is fixed. We present a polynomial-time algorithm for the problem which is based on the earlier introduced algorithmic framework blesscmore (``branch less and cut more''). We extend the analysis of the so-called behavior alternatives developed earlier for the version of the problem with identical parallel machines and show how the earlier used technique for identical machines can be extended to the uniform machine environment if a special condition on the job parameters is imposed. The time complexity of the proposed algorithm is $O(\gamma m^2 n\log n)$, where $\gamma$ can be either $n$ or the maximum job delivery time $q_{\max}$. This complexity can even be reduced further by using a smaller number $\kappa<n$ in the estimation describing the number of jobs of particular types. However, this number $\kappa$ becomes only known when the algorithm has terminated.

The overarching purpose of this review was to highlight the utility of different aerobic field tests in terms of the parameters they provide, with a specific focus on shuttle running and all-out testing. Various field tests are discussed in detail and are categorised according to linear continuous running tests (e.g. 12-minute Cooper Test, University of Montreal Track Test [UMTT], 1200/1600 m time trials, 3-minute all-out test for running [3MT]), intermittent shuttle running tests (e.g. yo-yo inter-mittent recovery test level 1 [YYIR1], 30-15 intermittent fitness test [IFT], and the intermittent all-out shuttle test [IAOST]), and continuous shuttle running tests (e.g. 1.2 km shuttle run test [1.2SRT], maximal multi-stage 20-m shuttle test [MSR], 25-m, 30 m and 50-m 3-minute all-out shuttle test [AOST]). Readers will be guided through the theoretical and practical underpinnings of the 3MT methodology, where the all-out testing methodology is stationed within the testing paradigm, and how to practically implement and interpret the results thereof.

Mechanical ventilation systems, which are used for breathing support when a person is not able to do it by their own, requires a device for measuring the air flow to the patient in order to monitoring and a assure the magnitude establish by a medical staff. Flow sensors are the conventional devices used for the air flow measuring; however, there were not available in Peru, because of the international demand during COVID-19 pandemic. In this sense, a novel air flow sensor based on orifice plate and an intelligent transducer stage were developed as an integrated design. Advanced methodologies in simulations and experiments using specially designed equipment for this application were carried out. The obtained data was used for a mathematical characterization and dimensions validation of the integrated design. The device was tested in its real working conditions, it was implemented in a breathing circuit connected to a low-cost mechanical ventilation system based on cams. Results indicate that the designed air flow sensor/transducer is a low-cost complete medical device for mechanical ventilators able to provide satisfactorily all the ventilation parameters air flow, pressure and volume over time by measuring the air flow and calculating the others. Furthermore, this device provides directly a filtered equivalent electrical signal for a display or a computer.

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.

The Kummer surface was constructed in 1864. It corresponds to the desingularisation of the quotient of a 4-torus by 16 complex double points. Kummer surface is kwown to play a role in some models of quantum gravity. Following our recent model of the DNA genetic code based on the irreducible characters of the finite group G5:=(240,105)≅Z5⋊2O (with 2O the binary octahedral group), we now find that groups G6:=(288,69)≅Z6⋊2O and G7:=(336,118)≅Z7⋊2O can be used as models of the symmetries in hexamer and heptamer proteins playing a vital role for some biological functions. Groups G6 and G7 are found to involve the Kummer surface in the structure of their character table. An analogy between quantum gravity and DNA/RNA packings is suggested.

Estimating the remaining useful life (RUL) of components is a crucial task to enhance the reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator ( ) to infer the current condition of the component, and modelling the degradation process, to estimate the future behavior. Although many signal processing and data-driven based methods were proposed to construct the , most of the existing methods are based on manual feature extraction techniques, and need the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the . For this purpose, the continuous wavelet transform (CWT) technique is used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model is trained by the healthy operation dataset. Finally, the Mahalanobis Distance (MD) between the healthy and failure stages is measured as the . The proposed method is tested on a benchmark bearing dataset and compared with several other traditional construction models. Experimental results indicate that the constructed exhibits a monotonically increasing degradation trend and has a good performance to detect incipient faults.

Currently there is no multi-hazard risk assessment tool for determining the level of complexity to swiftwater and flood rescue incidents. Traditionally, the International Scale of River Difficulty is used but it is primarily for paddlers for use in a recreational context, without much consideration to the multitude of hazards faced in swiftwater and flood rescue environments. In response to this gap, the ECHO risk assessment tool has been developed and undergone initial testing. This tool provides for simple and rapid codification of multiple hazards and response considerations and is globally applicable. The tool also assigns a final risk assessment colour making the interpretation of the assessment easy to understand and communicate. Though the proposed tool shows potential, further research is needed before it should be operationalised.

As the coproduct of steelmaking, steelmaking slag, volumetrically expansive-prone without treatment, has been satisfactorily used as an aggregate base course material in highway construction. Although numerous technical reports and papers have revealed that steel slag aggreged using in rigid matrixes, the end product, paving block, non-structural concrete, for example, possesses superb strength and durability properties, the application of steel slag aggregate as a concrete aggregate is currently prohibited in highway construction. Naturally, researchers explore the practicality of use of steel slag, how to connect the laboratory experiment results to the end-product behavior, and further convert the research results into real construction? For all nontraditional or secondary materials utilization, the procedure of laboratory material examination, lab and field trial test are critical. To generalize the utilization, usability criteria establishment is necessary. This paper tries to use step-by-step method and plain language to explain how the volumetrically expansion can be simulated in lab testing apparatus, and based on the model, the mathematical deduction can result in a numerical usability criterion for steel slag use as a coarse aggregate in concrete (a rigid material). Therefore the paper is composed four main parts, laboratory test methods to examine the expansion force resulted from steel slag; converting the measured expansion force in a given volume of mass aggregate to the expansion (body) force of a single particle; modeling of slag and end material disruption; and a usability criterion from numerical deduction for the use of expansive-prone coarse slag in concrete or other rigid matrices. The paper provides the solutions to answer the above questions. Through the mathematical and mechanical modeling, provides the potential criteria that could result in specifications and guidance establishment for broad volumetric expansive-prone slags.

Background: Traditional omic analysis relies on p-value and fold change as selection criteria. There is an ongoing debate on their effectiveness in delivering systemic and robust interpretation, due to their dependence on assumptions of conformity with various parametric distributions.Here, we propose a threshold-free selection method based on robust, non-parametric statistics, ensuring independence from the statistical distribution properties and broad applicability. Such methods could adapt to different initial data distributions, contrary to statistical techniques based on fixed thresholds. Methods: Our work extends the Rank Products methodology with a neutral selection method of high information-extraction capacity. We introduce the calculation of the RP distribution’s entropy to isolate the features of interest by their contribution to the distribution’s information content. The aim is a methodology performing threshold-free identification of the differentially expressed features, which are highly informative about the phenomenon under scrutiny. Conclusions: Applying the proposed method on microarray (transcriptomic and DNA methylation) and RNAseq count data of varying sizes and noise presence, we observe robust convergence for the different parameterisations to stable cutoff points. Functional analysis through BioInfoMiner and EnrichR was used to evaluate the information potency of the resulting feature lists. Overall, the derived functional terms provide a systemic description highly compatible with the results of traditional statistical hypothesis testing techniques. The methodology behaves consistently across different data types. The feature lists are compact and information-rich, indicating phenotypic aspects specific to the tissue and biological phenomenon i nvestigated. Selection by information content measures efficiently addresses problems, emerging from arbitrary thresholding, thus facilitating the full automation of the analysis.

To date electric pianos and samplers tend to concentrate on authenticity in terms of temporal and spectral aspects of sound. They barely recreate the original sound radiation characteristics, contribute to the perception of width and depth, vividness and voice separation, especially for instrumentalists, who are located in the near field. This paper describes an operational procedure to measure, store, and synthesize the complete sound of a harpsichord, including its spatial sound radiation characteristics. First, actuators excite the instrument at the intersection point of each string with the bridge with an exponential sine-sweep. Then, the radiated sound field is recorded in the near and the far field with microphone arrays. The pressure distribution in the near field is propagated back to the soundboard of the instrument, using Minimum Energy Method. The vibration of each single string is captured with lightweight contact microphones. The soundboard is then replaced by an array of 128 loudspeakers. The loudspeaker signal is a convolution of the back-propagated sweep recording with the string recording to perform a wave field synthesis. Above the spatial Nyquist frequency, the Radiation Method is applied to perform a sound field synthesis which is valid for the listening region of the instrumentalist. The result is an electric harpsichord, that approximates the sound of a real harpsichord precisely in time, frequency, and space domain. Applications for such a radiation keyboard are music performance, instrument and synthesizer building and interactive psychoacoustic research.

Calibrating ecological models or making decisions with them is an optimisation problem with challenging methodological issues. Depending on the optimisation formulation, there may be a large variety of optimisation configurations (e.g. multiple objectives, constraints, stochastic criteria) and finding a single acceptable solution may be difficult. The challenges are exacerbated by the high computational cost and the non linear or elusive mathematical properties that increased with the complexity of numerical models. From the feedbacks of practitioners, the need for a guideline for conducting optimisation of complex models has emerged. In this context, we propose a practical guide for the complex model optimisation process, covering both calibration and decision-making. The guide sets out the workflow with recommendations for each step based on existing tools and methods usually scattered throughout the literature. This guide is accompanied with an ODDO template (Overview, Design, Details of Optimisation) to standardise the published description of model-based optimisation and suggests research directions.

Opportunistic routing is the latest technique that uses the broadcasting nature of the wireless medium to increase the number of potential forwarding nodes in the network. This, in turn improves the delivery rate and reliability of data transmission in the network. Compared to all previous classes of routing protocols, opportunistic routing offers numerous advantages which is exploited by the latest applications for efficient communication and resource sharing in dynamic ad hoc networks. These applications provide dynamic communication in disaster recovery environments. The objective of this research work is to review and classify all the major opportunistic routing protocols proposed for dynamic ad hoc networks. Further the issues and challenges with each of these existing protocol is discussed and future research directions are put forward

This work presents some characteristics of MoNet, a computerized platform for the modeling and visualization of complex systems. Emphasis is on the ideas that allowed the successful progressive development of this modeling platform, which goes along with the implementation of applications to the modeling of several studied systems. The platform has the capacity to represent different aspects of systems modeled at different observation scales. This tool offers advantages in the sense of favoring the perception of the phenomenon of the emergence of information, associated with changes of scale. Some criteria used for the construction of this modeling platform are included. The power of current computers has made practical representing graphic resources such as shapes, line thickness, overlaying-text tags, colors and transparencies, in the graphical modeling of systems made up of many elements. By visualizing diagrams conveniently designed to highlight contrasts, these modeling platforms allow the recognition of patterns that drive our understanding of systems and their structure. Graphs that reflect the benefits of the tool regarding the visualization of systems at different scales of observation are presented to illustrate the application of the platform.

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

Indoor scene recognition has great significance for intelligent applications such as mobile robots, location-based services (LBS) and so on. Wherever we are or whatever we do, we are under a specific scene. The human brain can easily discern a scene with a quick glance. However, for a machine to achieve this purpose, on one hand, it often requires plenty of well-annotated data which is time-consuming and labor-intensive. On the other hand, it is hard to learn effective visual representations due to large intra-category variation and inter-categories similarity of indoor scenes. To solve these problems, in this paper, we adopted an unsupervised visual representation learning method which can learn from unlabeled data with a Siamese Convolutional Neural Network (Siamese ConvNet) and graph-based constraints. Specifically, we first mined relationships between unlabeled samples with a graph structure. And then, these relationships can be used as supervision for representation learning with a Siamese network. In this method, firstly, a k-NN graph would be constructed by taking each image as a node in the graph and its k nearest neighbors are linked to form the edges. Then, with this graph, cycle consistency and geodesic distance would be considered as criteria for positive and negative pairs mining respectively. In other words, by detecting cycles in the graph, images with large differences but in the same cycle can be considered as same category (positive pairs). By computing geodesic distance instead of Euclidean distance from one node to another, two nodes with large geodesic distance can be regarded as in different categories (negative pairs). After that, visual representations of indoor scenes can be learned by a Siamese network in an unsupervised manner with the mined pairs as inputs. In order to evaluate the proposed method, we tested it on two scene-centric datasets, MIT67 and Places365. Experiments with different number of categories have been conducted to excavate the potential of proposed method. The results demonstrated that semantic visual representations for indoor scenes can be learned in this unsupervised manner. In addition, with the learned visual representations, indoor scene recognition models trained with the learned representations and a few of labeled samples can achieve competitive performance compared to the state-of-the-art approaches.

Our understanding of the fate and distribution of micro- and nano- plastics in the marine environment and their impact on the biota compartment is limited by the intrinsic difficulties of conventional analytical techniques (light scattering, FT-IR, Raman, optical and electron microscopies) in the detection, quantification and chemical identification of small particles in liquid samples. Here we propose the use of optical tweezers, a technique awarded in 2018 with the Nobel prize, as an analytical tool for the study of micro- and nano- plastics in sea water. In particular, we exploit the combination of optical tweezers with Raman spectroscopy (Raman Tweezers, RTs) to optically trap plastic particles with sizes from tens of µm down to 90 nm and unambiguously reveal their chemical composition. RTs applications are shown on particles made of the most common plastic pollutants, including polyethylene, polypropylene, nylon and polystyrene, that are artificially fragmented and aged directly in seawater. RTs allow us to assess the size and shapes of microparticles (beads, fragments, fibers) and can be applied to investigate particles covered with organic layers. Furthermore, operating at the single particle level, RTs enable unambiguous distinction of plastic particles from marine microorganisms and seawater minerals, overcoming the capacities of standard Raman spectroscopy in liquid, limited to average measurements. Coupled to suitable extraction and concentration protocols, RTs could have a strong impact in the study of the fate of micro and nanoplastics in marine environment, as well as in the understanding of the fragmentation processes on a multi-scale level.

We study the performance of a classical and quantum magnetic Otto cycle with a quantum dot as a working substance using the Fock-Darwin model with the inclusion of the Zeeman interaction. Modulating an external/perpendicular magnetic field, we found in the classical approach an oscillating behavior in the total work that is not perceptible under the quantum formulation. Also, we compare the work and efficiency of this system for different regions of the Entropy, $S(T,B)$, diagram where we found that the quantum version of this engine always shows a reduced performance in comparison to his classical counterpart.

Indonesia has a large area of degraded land, i.e. 30 million ha, which could potentially be utilized for biofuel plantations. The leguminous tree pongamia (Pongamia pinnata syn. Milettia pinnata) could be utilized to produce biofuel while restoring degraded land. Here, we explore the potential of pongamia as a source of biofuel and for restoring degraded land in Indonesia. Pongamia occurs across Indonesia, in Sumatra, Java, Bali, West Nusa Tenggara and Maluku. It grows to a height of 15–20 m and can grow in a range of environmental conditions. Its seeds can generate up to 40% crude oil by weight. It can help to restore degraded land and improve soil properties. Pongamia also provides wood, fodder, medicine, fertilizer and biogas. Therefore, as a multipurpose species, pongamia holds great potential to combat Indonesia’s energy crisis and to restore much of the degraded land.

The extreme environmental issues and the resulting needs to save energy have turned the attention to electrification of energy applications. One of the key components involved in energy efficiency improvements is the appropriate conception and manufacturing of electric machines. This paper overviews the electromagnetic analysis governing the behavior of permanent magnets that enabled substantial efficiency gains in recent electric machine developments. Particular emphasis is given to model the properties and losses developed in permanent magnets in the emerging high speed applications. In addition, the investigation of properties and harmonic losses related to ferromagnetic materials constituting the machine magnetic circuits are equally analyzed and discussed. The experimental validation of the implemented methodologies and developed models with respect to the obtained precision is reported. The introduction of mixed numerical techniques based on the finite element method intended to appropriately represent the different physical phenomena encountered is outlined and discussed. Finally, fast and accurate simulation techniques including aggregated lumped parameter models considering harmonic losses associated with inverter supplies are commented.