Nowadays, the research and development of on-chip LiDAR sensors for vehicle collision avoidance is growing very fast. Therefore, the assessment of the reliability in obstacle detection using the information provided by LiDAR sensors has become a key issue to be explored by the scientific community. This paper presents the design and implementation of a self-tuning method in order to maximize the reliability of an Internet-of-Things sensors network and to minimize the number of sensors to localize with the required accuracy obstacles by a detection threshold. In order to achieve this goal, models that predict accuracy (i.e., prediction error) for object localization using data collected by LIDAR sensors are designed and implemented in Webots Automobile 3D simulation tool. The approach is based on combining different techniques. Firstly, point-cloud clustering technique and an error prediction model library composed by a multilayer perceptron neural network with backpropagation, k-nearest neighbors and linear regression are explored. Secondly the above-mentioned techniques for modeling are also combined with a supervised and reinforcement machine learning technique, Q-learning in order to minimize the detection threshold. In addition, a IoT driving assistance simulated scenario with a LiDAR sensor network is designed in order to validate the prediction model and the optimal configuration of the sensor network to guarantee reliability in obstacle localization. The results demonstrate that the self-tuning method is appropriate to increase the reliability of the sensor network whereas minimizing the detection threshold

The proposal that the genetic code was formed on the basis of protein synthesis directed by tRNA dimers is reviewed and updated. The tRNAs paired through the anticodon loops are an indication on the process. Dimers are considered mimics of the ribosomes – structures that hold tRNAs together and facilitate the transferase reaction, and of the translation process – anticodons are at the same time codons for each other. The primitive protein synthesis system gets stabilized when the product peptides are able to bind the producers therewith establishing a self-stimulating production cycle. The chronology of amino acid encoding starts with Glycine and Serine, indicating the metabolic support of the Glycine-Serine C1-assimilation pathway, which is also consistent with evidence on origins of bioenergetics mechanisms. Since it is not possible to reach for substrates simpler than C1 and compounds in the identified pathway are apt for generating the other central metabolic routes, it is considered that protein synthesis is the beginning and center of a succession of sink-effective mechanisms that drive the formation and evolution of the metabolic flow system. Plasticity and diversification of proteins construct the cellular system following the orientation given by the flow and implementing it. Nucleic acid monomers participate in bioenergetics and the polymers are conservative memory systems for the synthesis of proteins. Protoplasmic fission is the final sink-effective mechanism, part of cell reproduction, guaranteeing that proteins don’t accumulate to saturation, which would trigger inhibition.

Current theories of autoimmunity are diverse, sometimes contradictory, and suffer from incompleteness. Although substantial evidence exists that adaptive and innate immunity, sex, genetic predisposition, and the microbiome all play essential roles in autoimmune disease etiologies and pathogenesis, and that antigen processing is altered during disease induction, no existing theory integrates all of these factors through a single, coherent mechanism. In an attempt to focus the field on the need to elucidate such an integrative mechanism, I propose one possibility here that, if nothing else, helps to identify the nature of the problems that need to be addressed. My theory is that autoimmune diseases are induced by normal immunological responses to unique pairs of complementary antigens, at least one of which is a molecular mimic of a host target. Each antigen in the complementary pair induces a complementary immune response (T or B cell); although each immune response is idiotypic in origin, the antigenic complementarity results in what appears to be an idiotype-anti-idiotype relationship between the responses. Additionally, because of the antigenic complementarity, each immune response mimics one of antigens, abrogating the distinction between self and non-self. If at least one of the antigens mimics a host antigen, then the resulting immunological civil war spreads to a host tissue. Complementary antigens also alter antigen processing so that antigens that would normally be proteolytically digested are presented by the major histocompatibility complex (MHC) to T and B cell receptors inducing a cross-reactive immune response. The resulting civil war is supported by the innate immune system due to the complementarity of the initiating antigens.. Complementary antigens stimulate synergistic toll-like receptors (TLR) and/or nucleotide-binding oligomerization receptors (NOD) resulting in up-regulation of cytokine production and further stimulation of the adaptive immune response. Because the immune responses (e.g., antibodies) mimic the initiating antigens, this synergistic activation of innate immunity becomes chronic. Additionally, TLR and NOD function are highly sensitive to sex hormones, some becoming up-regulated and some down-regulated in the presence of either testosterone or estrogens. This sensitivity explains how sex modifies susceptibility to autoimmune diseases. Genetic mutations in TLR, NOD and MHC further alter antigen presentation and the degree to which antigens stimulate an immune response explaining how genetics also modifies susceptibility. Finally, sex hormones also alter the host microbiome, which in turn modulates autoimmune disease risk by shaping the immunological nature of self and by mediating susceptibility to microbial infection. Moreover, it appears that the microbiome camouflages itself from the immune system by mimicking the host antigenic repertoire; the mimicry between the antigens of the microbiome and the host results in selective attacks on microbiome constituents concomitant with any autoimmune attack on host tissues. This antigenic complementarity theory thereby integrates all major elements known to affect, or be affected by, autoimmune diseases and provides a set of testable implications.

The status of tropical fisheries is poorly known because the existing methodological choices have variable outcomes and recommendations. Therefore, multiple approaches to estimating sustainability were compared, namely fisheries independent stock biomass and recovery rates, fisheries dependent landed catches, balanced harvest and gear use metrics, and fish length measurements. Unfished biomass recovery time series was established by a 45-year stock recovery relationship from 7 fisheries reserves and compared to catch and length-based estimates of sustainability. The logistic production rates (r = 0.10 ± 0.06 95% CI) and maximum equilibrium total biomass (150 ± 30 tons/km²) indicated a broad range of potential maximum sustainable yields but likely ranging from 1.1 to 3.9 (95% CI; mean = 3.8) tons/km²/year. In contrast, mean annual biomass growth rates in reserves was lower but less variable than surplus production estimates, ranging from 2.1 to 3.5 (mean=2.8 tons/km²/year). Realized catches at landing sites was lower still, ranging from 1.43 to 1.52 (mean=1.48± 0.2 tons/km²/y). Differences were largely attributable to changes in taxonomic composition and an imbalance in the proportionality of production potential versus actual capture rates. Differences in the vulnerability of taxa to fishing and a lack of compensatory increased production among fishing resistant taxa could largely account for the lost potential production. Large proportional losses of catch were measured among snappers, unicorn fish, sweetlips, goatfish, and soldierfish, while smaller proportional gains in the catch were found among resident herbivorous rabbitfish, parrotfish, and groupers. Many of these declining taxa had vulnerable schooling life histories likely to require special habitat and reserve characteristics. Evaluations of sustainability from length measurements found 17 or 7% of total and 12% of caught species had a minimum sufficient sample sizes (>30 individuals, 413 catches, 2299 individuals, and 144 species) to evaluate length and spawning metrics of sustainability. Seven of these species met length-based and 3 meet spawning potential ratio thresholds for sustainability. Consequently, length-based evaluations were unable to evaluate the sustainability of the larger community. Recommendations include a management focus on the recovery of “ghost taxa”, species abundant in the oldest reserve but not contributing to fisheries yields.

The potato is the most important non-cereal food crop, and thus improving potato growth and yield is in the focus of agricultural researchers and practitioners worldwide. Several studies reported beneficial effects of silicon (Si) fertilization on potato performance, although plant species from the family Solanaceae are generally considered as non-Si-accumulating. We used results from two field experiments in the temperate zone to gain insight into silica accumulation in potato plants as well as corresponding long-term potato yield performance. We found relatively low Si contents in potato leaves and roots (up to 0.08% and 0.3% in the dry mass, respectively) and negligible Si contents in potato tuber skin and tuber flesh for plants grown in soils with different concentrations of plant available Si (field experiment 1). Moreover, potato yield was not correlated to plant available Si concentrations in soils in the long-term (1965-2015, field experiment 2). Based on our results we ascribe the beneficial effects of Si fertilization on potato growth and yield performance reported in previous studies mainly to antifungal/osmotic effects of foliar-applied Si fertilizers and to changes in physicochemical soil properties (e.g., enhanced phosphorus availability and water holding capacity) caused by soil-applied Si fertilizers.

Many of the fundamental molecules of life share extraordinary pigment-like optical properties in the long-wavelength UV-C spectral region. These include strong photon absorption and rapid (sub pico-second) dissipation of the induced electronic excitation energy into heat through peaked conical intersections. These properties have been attributed to a ``natural selection'' of molecules resistant to the dangerous UV-C light incident on Earth's surface during the Archean. In contrast, the ``thermodynamic dissipation theory for the origin of life'' argues that, far from being detrimental, UV-C light was, in fact, the thermodynamic potential driving the dissipative structuring of life at its origin. The optical properties were thus the thermodynamic ``design goals'' of microscopic dissipative structuring of organic UV-C pigments, today known as the ``fundamental molecules of life'', from common precursors under this light. This ``UV-C Pigment World'' evolved towards greater solar photon dissipation through more complex dissipative structuring pathways, eventually producing visible pigments to dissipate less energetic, but higher intensity, visible photons, up to wavelengths of the ``red-edge''. The propagation and dispersal of organic pigments, catalyzed by animals, and their coupling with abiotic dissipative processes, such as the water cycle, culminated in the apex photon dissipative structure, today's biosphere.

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.

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

Zika virus (ZIKV) infection and pathogenesis are linked to the disruption of neurogenesis, congenital Zika syndrome and microcephaly by affecting neural progenitor cells. Nonstructural protein 5 (NS5) is the largest product encoded by ZIKV-RNA and is important for replication and immune evasion. Here, we studied the potential effects of NS5 on microtubules (MTs) and autophagy flux, together with the interplay of NS5 with histone deacetylase 6 (HDAC6). Fluorescence microscopy, biochemical cell-fractionation combined with the use of HDAC6 mutants, chemical inhibitors and RNA interference indicated that NS5 accumulates in nuclear structures and strongly promotes the acetylation of MTs that aberrantly reorganize in nested-like structures. Similarly, NS5 accumulates the p62 protein, an autophagic-flux marker. Therefore, NS5 alters events that are under the control of the autophagic tubulin-deacetylase HDAC6. HDAC6 appears to degrade NS5 by autophagy in a deacetylase- and BUZ domain-dependent manner and to control the cytoplasmic expression of NS5. Moreover, NS5 inhibits RNA-mediated RIG-I interferon (IFN) production, resulting in greater activity when autophagy is inhibited (i.e., effect correlated with NS5 stability). Therefore, it is conceivable that NS5 contributes to cell toxicity and pathogenesis, evading the IFN-immune response by overcoming HDAC6 functions. HDAC6 has emerged as an anti-ZIKV factor by targeting NS5.

This research aims to understand the Cambodian green economy transition and analyze its strengths, weaknesses, opportunities, and threats (SWOT). The research showed that Cambodia has been significantly transitioning its green economy since 2009 as evidenced by the National Green Growth Roadmap. Cambodia also established the National Council for Green Growth in 2012 and upgraded it to the National Council for Sustainable Development in 2015 in alignment with 2030 Agenda for Sustainable Development of the United Nations. The members of the Council are from all the government’s ministries and relevant inter-ministerial councils/committees, which are significant for policymaking. All governors of the capital and provincial administrations are also its members, which are significant for supporting and implementing the works at the subnational levels. Under the structure of the Council’s Secretariat, Department of Green Economy is playing an important role in promoting green economic development in Cambodia by looking at national policies and strategic plans, undertaking projects, and mobilizing resources. Through coordination between this Department and UNIDO, Cambodia in 2022 became a Member of the United Nations Partnership for Action on Green Economy. The findings showed that Cambodia is transitioning its green economy at the right time, especially the new government’s Pentagonal Strategy strongly supported this green economy transition with a clear and strong political commitment. The national inter-ministerial council for sustainable development is also very significant for cross sectoral policy-making and implementing levels, inclusively for promoting green economic development.

India, the world’s most populous country, is the world's third-largest emitter of greenhouse gases (GHGs). Despite employing several energy sources, it still relies heavily on coal, its primary energy source. Given India’s swiftly rising energy demand, this challenges meeting emission reduction targets. India has significantly increased investments in renewables like solar and hydrogen in recent years. While commendable, these initiatives alone cannot meet the country's expanding energy demands. In the short term, India must rely on both domestic and imported fossil fuels, with natural gas being the most environmentally friendly option. In this context, this paper attempts to forecast energy consumption, natural gas production, and consumption in India till 2050 using both univariate and multivariate forecasting methods. For multivariate forecasting, we have assumed two alternative possibilities for GDP growth: the business-as-usual and the high-growth scenarios. Each of our forecasts indicates a notable shortfall in the projected production of natural gas compared to the expected demand, implying our results are robust. Our model predicts that nearly 30-50 percent of India’s natural gas consumption will be met by imports, mainly in the form of LNG. Based on these findings, this paper recommends that Indian government policies emphasize increasing domestic natural gas production, importing LNG, and expanding renewable energy resources.

The study was aimed at examining the status of research productivity and exploring the factors behind the identified status in Ethiopian universities from SDT and RBVT perspectives. In phase I, a null hypothesis was tested using a one-sample t-test at 0.05 alphas while in phase II one RQ was solved using qualitative analysis. The sample involved 531 academics, of whom 13 were interviewed. The mixed-methods research approach and explanatory sequential design guided this research. The status of research project production, indexed publication, and conference presentation performances in Ethiopian universities demonstrated statistically significant mean differences below the institutional, national, and world average cut-off points (performing at least one research project, one indexed journal, and one conference paper presentation per year per academic staff). Furthermore, we explored scarcity of funds, low research capacity, weak research culture, lack of productive institutional leadership, and political instability in the country as major causes of the low research productivity status. The researchers felt that the study would contribute to both promoting academic staff's research productivity and enriching the higher education research literature. MOE, university presidents, and deans should intervene in causes to improve academics’ research productivity status towards the desired local and global standards.

Thrombosis is characterized by the pathological formation of fibrin clots within a blood vessel, leading to the obstruction of blood flow. Fibrinolytic enzymes from microorganisms have exhibited promising effects to dissolve clots in a more efficient and safe way. Then, the aim of this study was to evaluate the biomass and fibrinolytic enzyme production of Tetradesmus obliquus under autotrophic and mixotrophic conditions using different concentration of corn steep liquor (CSL). Different extraction and precipitating methods were tested, and the enzyme was purified by ion exchange chromatography. More advantageous culture condition was mixotrophic using 0.25% CSL, showing the highest values of biomass productivity (Px = 169.3 ± 44.36 mg∙L-1day-1) and specific growth rate (µmax = 0.17 ± 0.00 day-1), and significant fibrinolytic production (391.34 ± 40.03 U∙mg-1). Moreover, fibrinolytic activity was higher when extracted by homogenization and precipitated using acetone, which exhibited clear zone of fibrin degradation in the fibrinolytic plate assay. Additionally, the purified enzyme showed specific activity of 1176.90 ± 140.37 U∙mg-1 and molecular weight around 97 kDa. Finally, the enzyme has higher enzymatic activity than various fibrinolytic enzymes, and the obtained enzyme has potential to be developed as a therapeutic agent in thrombosis treatment. Additional studies are need to investigate the biochemical properties and biological profile of this enzyme.

The links between rural and urban have often been the subject of study in Portugal. Nevertheless, the asymmetries between rural and urban have not ceased to increase over the last decades, contributing to an imbalance of economic, social, and territorial dimensions between regions. In order to counter this trend, policies to attract the population and tourism have been endorsed but have not been sufficient to promote territorial cohesion. This makes it necessary to rethink the capacity and the potential of rural territories in terms of mobilising their resources and, through them, promoting new and/or renewed relations with urban territories. This article aims to explore and map the links between urban and rural areas by analysing the routes taken by agri-food products, from production to marketing, with the support of a spatial analysis, using Geographic Information Systems (GIS). For that, we analysed a set of specialised and/or gourmet shops in Oorto and Lisbon, and their relations with the territories from which the commercialized agri-food products come. Most of these products originate in the inland rural regions of the country, and there is a clear geographical connection between the shops analysed and specific territories. Thus, it was possible to verify the increase in interest and consumption of agri-food products of rural origin and the spontaneous increase of this type of shop in the cities analysed, as well as identify the target audience of the shops and the spatial influence they have in the respective cities. These shops, in addition to selling agro-food products, are also promoters of rural territories in an urban environment.

The western Sulige gas field is a new and key reserve area for the rolling development of the Sulige gas field in China. However, due to the complex gas–water relationship and the difficulty in identifying gas and water formation, the scale and benefit deployment of the gas field are seriously restricted. In particular, almost all of the wells in the area produce water and no water measurement has been carried out for any single well, which leads to an unclear understanding of the dynamic characteristics of the production wells, thus affecting the productivity calculations of the gas wells and the overall regional productivity evaluation. Based on the testing data for a gas well, the impacts of the reservoir property parameters on the gas and water production were analyzed by combining the production performance and static geological characteristics. It was determined that the physical parameters K, Kh, and φSg had good positive correlations with gas production but not with water production; thus, effective prediction cannot be obtained for water production in gas well testing. After the analysis of the liquid loading law, the gas wells were classified into three types: continuous liquid carrying production, slight liquid loading, and liquid loading wells. In general, up to 96% of the gas wells were liquid loaded. According to the different production performances exhibited in the different stages of the gas wells, five types of methods for diagnosing water production wells were proposed (gas testing, pilot production, gas–liquid two-phase measurement testing, liquid level detection, and production performance analysis), as well as the diagnostic criteria and corresponding solutions. To obtain real-time water production data for each well and investigate the change of water-gas ratio (WGR) during the whole production process, a water production splitting method for gas wells based on three-dimensional geological modeling and numerical simulation combined with the constraints of the total water production of gas gathering stations was explored and established. The splitting results can be used to evaluate the water and gas productivity of gas wells and determine the best deliquification period. The gas well productivity when water production was considered was about 10% lower than that when water production was not considered. The best deliquification period was determined to be 125 days for wells with small water production, 20 days for wells with moderate water production, and 3 days for wells with serious water production. The results of this study could provide technical support for the scientific evaluation of gas well production indicators, reduction of development costs, and improvement of oil recovery.

Ethanol produced from various biobased sources (bioethanol) has been gaining high attention lately due to its potential to cut down net emissions of carbon dioxide while reducing burgeoning world dependence on fossil fuels. Global ethanol production has increased more than six-fold from 18 billion liters at the turn of the century to 110 billion liters in 2019 (1,2). Sugar cane and corn have been used as the major feedstocks for ethanol production. Lignocellulosic biomass has recently been considered as another potential feedstock. This paper reviews recent developments and current status of commercial production of ethanol across the world. The review includes the ethanol production processes used for each type of feedstock, both currently practiced at commercial scale and newly developed technologies, and production trends in various regions and countries in the world.

Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap where human factor was seen as an important source of errors and disruptions. Today the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised the awareness about the central role humans have to play in manufacturing systems, to the design of which they must be considered from the very beginning. The future of industrial systems, as represented by Industry 4.0, will rely on the convergence of several research fields such as Intelligent Manufacturing Systems (IMS), Cyber-Physical Systems (CPS), Internet of things (IoT), but also socio-technical fields such as social approaches within technical systems. This article deals with different Human dimensions associated with CPS and IoT and focuses on their conceptual evolution of automatization to improve the sociability of such automated production systems and consequently puts again the human in the loop. Hereby, our aim is to take stock of current research trends, and to show the importance of integrating human operators as a part of a socio-technical system based autonomous and intelligent products or resources. As results, different models of sociability as way to integrate human into the broad sense and/or the development of future automated production systems, were identified from the literature and analysed.

Water stable proteins may offer a new field of applications in smart materials for buildings and infrastructures where hydraulic reactions are involved. In this study, cement mortars modified through water-soluble silk fibroin (SF) are proposed. Water-soluble SF obtained by redissolving SF films in phosphate buffer solution (PBS) showed the formation of a gel with β-sheet features of silk II. Electrical measurements on SF indicate that calcium ions are primarily involved in the conductivity mechanism. By exploiting the water solubility properties of silk II and Ca2+ ion transport phenomena and its trapping effect over water molecules, SF provides piezoresistive and piezocapacitive properties to cement mortars, thus enabling self-sensing of mechanical strain which is quite attractive in structural health monitoring applications. SF/cement-based composite introduces the capacitive gauge factor that surpasses the traditional resistive gauge factor reported in the literature by threefold. Cyclic voltammetry measurements demonstrated SF cement mortars possess memcapacitive behavior for positive potentials around +5 V, attributed to an interfacial charge build-up modulated by the SF concentration and the working electrode. Electrical square-biphasic excitation combined with cyclic compressive loads revealed memristive behavior during the unloading stages. These findings along with the availability and the sustainability of SF pave the way for the design of novel multifunctional materials, particularly for applications in masonry and concrete structures.

Living systems are capable on the one hand, of eliciting a coordinated response to changing environment (aka adaptation) and on the other hand, they are capable of reproducing themselves. Notably, adaptation to environmental change requires the monitoring of the surroundings, while reproduction requires monitoring oneself. These two tasks appear separate and making use of different sources of information. Yet both the process of adaptation as well as that of reproduction are inextricably coupled to alterations of genomic DNA expression, while a cell behaves as an indivisible unity in which apparently independent processes and mechanisms are both integrated and coordinated. We argue that at the most basic level, this integration is enabled by the unique property of the DNA to act as a double coding device harboring two logically distinct types of information. We review biological systems of different complexity and infer that inter-conversion of these two distinct types of DNA information represents a fundamental self-referential device underlying both systemic integration and coordinated adaptive response.

Integrating ecosystem services (ESs) to delineate ecological functional zones (EFZs) is a fundamental work in terrestrial spatial planning and ecosystem management. However, existing studies have largely overlooked the refinement of EFZs at local scales, which hinders targeted and multi-scale ecosystem management. This study introduced a "two-step refinement zoning method" to address this gap, first using the self-organizing feature mapping method to delineate EFZs based on ESs at a township scale, and then applying hotspot overlay analysis to identify hotspots of dominant ESs within EFZs at a village scale. By overlaying the outcomes of these two steps, EFZs can be further graded by designating villages with hotspots of dominant ESs as level I and those without as level II. The proposed method was applied in Wuhan City, dividing it into five types of EFZs with different ESs combinations and land use compositions. Furthermore, 5.23% of villages were identified as Level I of EFZs, serving as advantageous areas of dominant ESs in the study area. On this basis, diversified management strategies and conservation priorities were proposed. This study provides a theoretical and methodological reference for terrestrial spatial planning and sustainable ecosystem management.

This study explored the effectiveness of a virtual music therapy program, based on positive psychotherapy principles, for college students during the COVID-19 pandemic. Twenty-four undergraduate students with partial PTSD were initially assigned to either an experimental group or a control group, with 11 participants in each group by the study's end. The experimental group underwent 15 video sessions of the therapy program, completing one session per weekday over three weeks. The program involved worksheets targeting goals aligned with positive psychology, such as positive affect, life meaning, personal strengths, gratitude, hope, and happiness. Activities included writing music autobiographies, creating and analyzing song lyrics, and exploring various music pieces. The effectiveness of the intervention was measured using the 21-item Depression Anxiety Stress Scale and the Korean Version of Positive Psychological Capital (K-PPC) before, immediately after, and three weeks post-program. The experimental group showed significant improvements in stress, anxiety, depression, self-efficacy, resilience, and the K-PPC total score compared to the control group. These improvements were maintained at the three-week follow-up. The findings suggest that positive psychology-based virtual music therapy can significantly enhance the mental health of highly stressed college students, especially during challenging times like the COVID-19 pandemic.

: This study aimed to validate the effects of self-compassion-based forest healing therapy as an intervention for reducing stress and promoting coping resources. Therefore, we investigated the effects of self-compassion-based forest healing therapy on stress, self-compassion, and connectedness to nature. A total of 30 participants were divided into an experimental and control group. Two sessions of forest healing meditation activities were held at the Forest of Healing every two weeks between October and November 2023. As a repeated measures experiment, data were collected before the intervention, after the first intervention, and after the second intervention. Perceived stress, positive and negative affect, and salivary cortisol were measured to assess physiological and psychological stress in participants. Connectedness to nature and self-compassion were measured to evaluate stress-coping resources. The results showed that the forest healing program had significant positive effects on stress and emotional states in the experimental group after the intervention. Further, there was a significant incremental effect on connectedness to nature and self-compassion in the experimental group. Therefore, self-compassion meditation-based forest healing therapy is a feasible and efficient approach for facilitating stress management and coping resource building.

A theoretical account of development in mesocortical anatomy is derived from the free energy principle, operating in a neural field with both Hebbian and anti-Hebbian neural plasticity. An elementary structural unit is proposed, in which synaptic connections at mesoscale are arranged in paired patterns with mirror symmetry. Exchanges of synaptic flux in each pattern form coupled spatial eigenmodes, and the line of mirror reflection between the paired patterns operates as a Markov blanket, so that prediction errors in exchanges between the pairs are minimized. The theoretical analysis is then compared to the outcomes from a biological model of cortical neurodevelopment, in which neuron precursors are selected by apoptosis for cell body and synaptic connections maximizing synchrony and also minimizing axonal length. It is shown that this model results in patterns of connection with the anticipated mirror symmetries, at micro-, meso- and inter-arial scales, among lateral connections, and in cortical depth, explaining the spatial organization and functional significance of neuron response preferences, and compatible with features of both columnar and noncolumnar cortex. Multi-way interactions of mirrored representations provide a preliminary anatomically realistic model of cortical information processing.

This article introduces existential wellbeing (EWB) as one of the most ignored but also the most important kind of wellbeing, especially for people who experience life-limiting diseases and need palliative care (Wong & Yu, 2021). This article will not only review the literature regarding the nature and practice of existential wellbeing in palliative care, but also propose a preliminary Existential Wellbeing Scale. EWB is defined as the quality-of-life characterized by living well and dying well in spite of pain, obstacles, and the shadow of death; wellbeing within the context of human suffering can only be fully understood from the perspective of existential positive psychology (EPP), dedicated to the study of sustainable wellbeing based on the foundation of transcending suffering. The basic tenets of EPP are (1) redefining positivity or wellbeing in terms of seeing and being the light in the darkness; (2) focusing on the dialectical process of navigating an adaptive balance; and (3) re-orienting one’s life attitude from egotism to self-transcendence (Wong, 2023a). More specifically, EWB depends on some combination of the following experiences: (1) Prior experience of overcoming adversity; (2) Existential intelligence (Gardner, 2020) in addressing existential concerns; (3) Existential wisdom of the soul (Wong 2022a; Wong, Ho, et al, 2023a) to achieve existential wellbeing and sustainable flourishing; (4) Spiritual wellbeing based on spiritual values such as faith, hope, and love (Wong, 2023); (5) Knowledge of meaning-focused coping (Eisenbeck et al., 2021); and (6) The new paradigm of self-transcendence (Reed & Haugan, 2021; Wong, Arslan et al., 2021; Wong, Ho et al., 2023b) to transcend and transform suffering into wellbeing and triumph.

Matter grows and self-assembles to produce complex structures such as virus capsids, carbon fullerenes, proteins, glasses, etc. Due to its complexity, performing pen-and-paper calculations to explain and describe such assemblies is cumbersome. Many years ago, Richard Kerner presented a pen-and-paper path integral approach to understand self-organized matter. Although successfully addressed many important problems including the yield of fullerene formation, the glass transition temperature of doped chalcogenide glasses, the fraction of boroxol rings in B2O3 glasses, the first theoretical explanation for the empirical recipe of window and Pyrex glass and the understanding of virus capsid self-assembly, still is not the primary choice when tackling similar problems. The reason lies in the fact that it diverges from mainstream approaches based on the energy landscape paradigm and non-equilibrium thermodynamics. In this context, a critical review is presented, demonstrating that the Richard Kerner method is, in fact, a clever way to identify relevant configurations. Its equations are simplified, common physical sense versions to those found in the energy landscape kinetic equations. Subsequently, the utilization of equilibrium Boltzmann factors in the transition Markov chain probabilities is analyzed within the context of local two-level energy landscape models kinetics. This analysis demonstrates that their use remains valid when the local energy barrier between reaction coordinate states is small compared to the thermal energy. This finding places the Richard Kerner model on par with other more sophisticated methods and, hopefully, will promote its adoption as an initial and useful choice for describing the self-agglomeration of matter.

The impact of the COVID-19 pandemic and the quarantine measures on children and adolescents’ hospitalization psychiatric has been poorly studied, especially in Italy, the first European country to adopt social restrictions. To fill this gap, this retrospective study examined changes in admissions to our child and adolescent psychiatry ward from the emergency department during the two COVID-19 quarantine periods in Italy (March to June 2020; October 2020 to January 2021) compared with two reference periods (defined as 12 months prior to the respective quarantine periods). Specifically, we examined data on the number of inpatients and the distribution of psychopathological disorders (i.e., psychosis, mood disorder) and self-injurious behaviors. The results revealed significantly fewer admissions during the first quarantine period relative to the first reference period and, conversely, significantly more admissions during the second quarantine period relative to the second reference period. Of note, the results showed an increased frequency of mood disorders, non-suicidal self-injurious behaviors and suicidal ideation during the quarantine periods, compared to the reference periods. The findings underline the need to focus on the psychopathological profile of children and adolescents with patient-tailored therapeutic interventions and suggest the importance of developing psychological healthcare services for future emergency periods..

Research on image-inpainting tasks has mainly focused on enhancing performance by augmenting various stages and modules. However, this trend does not consider the increase in the number of model parameters and operational memory, which increases the burden on computational resources. To solve this problem, we propose a Parametric Efficient Image InPainting Network (PEIPNet) for efficient and effective image-inpainting. Unlike other state-of-the-art methods, the proposed model has a one-stage inpainting framework in which depthwise and pointwise convolutions are adopted to reduce the number of parameters and computational costs. To generate semantically appealing results, we selected three unique components: spatially-adaptive denormalization (SPADE), dense dilated convolution module (DDCM), and efficient self-attention (ESA). The SPADE was adopted to conditionally normalize activations according to the mask to distinguish between damaged and undamaged regions. The DDCM was employed at every scale to overcome the gradient-vanishing obstacle and gradually fill-in pixels by capturing global information along the feature maps. The ESA was utilized to obtain clues from unmasked areas by extracting long-range information. In terms of efficiency, our model has the lowest operational memory compared with other state-of-the-art methods. Both qualitative and quantitative experiments demonstrate the generalized inpainting of our method on three public datasets: Paris StreetView, CelebA, and Places2.

Neurodegeneration is becoming one of the leading causes of death worldwide as the population expands and grows older. There is a growing desire to understand the mechanisms behind prion proteins as well as the prion-like proteins that make up neurodegenerative diseases (NDs) including Alzheimer’s Disease (AD). Both amyloid β (Aβ) and hyperphosphorylated tau (p-tau) proteins behave in ways similar to that of the infectious form of the prion protein, PrPSc, such as aggregating, seeding, and replicating under not yet fully understood mechanisms, thus the designation of prion-like. This review aims to highlight the shared mechanisms between prion-like proteins and prion proteins in the structural variations associated with aggregation and disease development. These mechanisms are largely focusing on the dysregulation of protein homeostasis, self-replication, and protein aggregation, and how this knowledge could contribute to diagnoses and treatments for the given NDs.

The vast majority of autonomous driving systems are limited to applications on roads with clear lane markings and are implemented using commercial grade sensing systems coupled with specialized graphic accelerator hardware. This research reviews an alternative approach for autonomously steering vehicles that eliminates the dependency on road markings and specialized hardware. A combination of machine vision, machine learning and artificial intelligence based on popular pre-trained Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) was used to drive a vehicle along roads lacking lane markings (unmarked roads). The team developed and tested this approach on the ACTor (Autonomous Camus TranspORt) vehicle - an autonomous vehicle development and research platform coupled with a low-cost webcam based sensing system and minimal computational resources. The proposed solution was evaluated on real-world roads and varying environmental conditions. It was found that this solution may be used to successfully navigate unmarked roads autonomously with acceptable road following behavior.

Durability of concrete requires a dense microstructure which can be achieved by using self-compacting concrete (SCC). Both calcined clay (CC) and rice husk ash (RHA) are promising supplementary cementitious materials (SCMs) that can partially replace cement, but their use in SCC is critical due to their higher water demand and specific surface area (SSA) compared to cement. Empirical method of SCC design was adopted considering the physical properties of both CC and RHA. The influence of partially substituting cement at 20 vol-% with binary and ternary blends of CC and RHA were investigated. The fresh properties of SCC were investigated using a variety of tests. The time dependent workability was monitored by plunger method and flow resistance determined based on the rheological measurements of SCC. The evolution of the hydrate phases of the binder in SCC was determined by thermogravimetric analysis, while the durability was evaluated by rapid chloride migration test. Cement partial replacement with 20 vol-% CC has no significant effect on SCC fresh, time dependent, compressive strength and durability properties. 20 vol-% RHA on the other hand requires higher dosage of SP to achieve self-compactability and increased the viscosity of SCC. Its workability retention is only up to 30 min after mixing and exhibited higher flow resistance. It consumes more CH and improves compressive strength and chloride resistance of SCC. The ternary blending with CC and RHA yielded better fresh SCC properties compared to the binary blend with RHA, while an improved chloride penetration resistance could be achieved compared to binary CC blend.

The multivariate Gaussian random fields with matrix-based scaling laws are widely used for inference in statistics and many applied areas. In such contexts interests are often in symmetry and in the rates of change of spatial surfaces in any given direction. This article analyzes the almost sure sample function behavior for operator fractional Brownian motion, including multivariate fractional Brownian motion. We obtain the estimations of small ball probability and the strongly locally nondeterministic for operator fractional Brownian motion in any given direction. Applying these estimates we obtain Chung's laws of the iterated logarithm for spatial surfaces of operator fractional Brownian motion. Our results show that the precise rates of change of spatial surfaces are completely determined by the self-similarity exponent.

The ability to self-direct a research program determines graduate degree completion. Yet, research on incompletion of graduate physics programs assume students’ present level of self-direction adequate and neglects to recognize a lack of self-directed learning as key. One theoretical mathematical physicist focused on changing this challenge of physics graduate education by promoting self-directed learning through the type research flow that has been found to bring the greatest satisfaction to researchers with respect to their insights. This he provided through his space, time, open mindedness and theoretical contributions with his students and in collaboration with his colleagues. A self-directed learner himself, David J. Rowe developed methods of mentoring for encouraging physics graduate students to recognize symmetry as valuable in identifying solutions to problems quickly—helping these students take the lead in finding insightful resolutions to complex, multidimensional, mathematical physics uncertainties. How Rowe set about supporting self-directed learning in his graduate physics education interactions will be examined with the use of narrative research to interpret the texts and conversations with the author he made available. His techniques will be presented and recommendations made regarding how Rowe’s work in this regard can be modeled to improve self-direction in STEM graduate education.

Branching patterns are ubiquitous in nature, consequently over the years many researchers have tried to characterize the complexity of their structures. Due to their hierarchical nature and resemblance to fractal trees, they are often thought to have fractal properties, however their non-homogeneity (i.e., lack of strict self-similarity) is often ignored. In this paper we review and examine the use of the box-counting and sandbox methods to estimate the fractal dimensions of branching structures. We highlight the fact that these methods rely on an assumption of self-similarity that is not present in branching structures due to their non-homogeneous nature. Looking at the local slopes of the log-log plots used by these methods reveals the problems caused by the non-homogeneity. Finally, we examine the role of the canopies (endpoints or limit points) of branching structures in the estimation of their fractal dimensions.

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

A sustainable solution for crack maintenance in geopolymers is necessary if they are to be the future of modern green construction. This study thus aimed to develop self-healing biogeopolymers that could potentially rival bioconcrete. First, a suitable healing agent was selected from Bacillus subtilis, B. sphaericus, and B. megaterium by directly adding their spores in the geopolymers and subsequently exposing them to a large amount of nutrients for 14 days. SEM-EDX analysis revealed the formation of biominerals for B. subtilis and B. sphaericus. Next, the effect of biochar-immobilization and co-culturing (B. sphaericus and B. thuringiensis) on the healing efficiencies of the geopolymers were tested and optimized by measuring their ultrasonic pulse velocities weekly over a 28-day healing period. The results show that using co-cultured bacteria significantly improved the observed efficiencies, while biochar-immobilization had a weak effect but yielded an optimum response between 0.3-0.4 g/mL. The maximum crack width sealed was 0.65 mm. Through SEM-EDX and FTIR analyses, the biominerals precipitated in the cracks were identified to be mainly CaCO3. Furthermore, image analysis of the XCT scans of some of the healed geopolymers confirmed that their pulse velocities were indeed improving due to the filling of their internal spaces with biominerals. With that, there is potential in developing self-healing biogeopolymers using biochar-immobilized spores of bacterial cultures.

Transparent binder is used to substitute conventional black asphalt binder and to provide light-colored pavements, whereas nano-TiO2 has the potential to promote photocatalytic and self-cleaning properties. Together, these materials provide multifunction effects and benefits when the pavement is submitted to high solar irradiation. This paper analyses the physicochemical and rheological properties of a transparent binder modified with 0.5%, 3.0%, 6.0%, and 10.0% of nano-TiO2 and compares it to the transparent base binder, and conventional and polymer modified binders (PMB) without nano-TiO2. Their penetration, softening point, dynamic viscosity, master curve, black diagram, Linear Amplitude Sweep (LAS), Multiple Stress Creep Recovery (MSCR), and Fourier-Transform Infrared Spectroscopy (FTIR) were obtained. The transparent binders (base and modified) seem to be workable considering their viscosity and exhibited values between the conventional binder and PMB regarding rutting resistance, penetration, and softening point. They showed similar behavior as the PMB, demonstrating signs of polymer-modification. The addition of TiO2 seems to reduce fatigue life, except for the 0.5% content. Nevertheless, its addition in high contents increases the rutting resistance. The TiO2 modification seems to have little effect on the chemical functional indices. The best percentage of TiO2 was 0.5%, considering fatigue and 10.0% concerning permanent deformation.

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in factory, such as the Industrial Internet of Things (IIoT) and Cloud Manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

Background: Parents of preterm infants face major mental health challenges in the Neonatal Intensive Care Unit (NICU). Family-centered music therapy actively integrates and empowers parents in their infant’s care. With the aim to better understand and address parental needs separately from their babies’, a music therapy (MT) self-care group was implemented as part of clinical practice at the hospital Clínica de la Mujer in Bogotá, Colombia. Methods: The group is provided for both parents twice a week in the NICU. Music guided relaxations, breathing techniques, and self-expression are at the center of the MT group sessions. Parents complete a pre/post self-administered Numeric Rating Scale (NRS) including anxiety levels, stress levels, mood and motivation. Results: Parents highly value the MT self-care group at the NICU. On average there is a 37% improvement in anxiety levels, 28% in stress levels, and 12% in mood, restfulness and motivation. Being able to relax, to distract themselves from their worries and having time for themselves are amongst the most frequently mentioned benefits. Conclusions: Addressing parents’ needs separately form their babies’ treatment with culturally sensitive interventions aimed to improve parental mental health, is essential for continuing the development of family-centered music therapy interventions in the NICU.

Using the culture-work-health model, this study investigates the factors influencing the quality of life of maritime workers. This study conducted a survey of 320 maritime workers who have experience living and working on a ship for more than six months. This self-administered questionnaire included questions on organizational culture and support, self-efficacy, perceived fatigue, as well as the quality of work life. Organizational culture and self-efficacy were identified as factors affecting the quality of work life, while organizational support was found to have an indirect effect after passing through self-efficacy and perceived fatigue. The final model accounts for 63.1% of the variance in maritime workers’ quality of life. As such, this study shows that self-efficacy is important for the quality of life of maritime workers, having both direct and indirect effects. Moreover, organizational support may prove the primary intervention point for relieving perceived fatigue and enhancing self-efficacy, thus improving the quality of work life.

This study explores individual and contextual risk factors for the onset of work interfering with private life (WIL) and private life interfering with work (LIW) among self-employed men and women across European countries. It also studies the relationship between interference (LIW and WIL) and wellbeing among self-employed men and women and the effect of macro level risk factors. Data from the fifth round of European Working Conditions Survey was utilized and a sample of self-employed men and women with active businesses was extracted. Applying multilevel regressions, results show that though business characteristics are important for level of WIL, time demand is the most evident risk factor for WIL and LIW. There is a relationship between wellbeing and WIL and LIW respectively, and time demands is the most important factor in this relationship. Gender equality on the labor market did not relate to level of interference, nor did it mediate the relationship between interference and wellbeing. However, the main and most important risk factor for experiencing WIL and LIW and for how interference relate to wellbeing is gender relation processes in work and life, both on individual and contextual level.

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix materials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNTs content. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both static and dynamically varying compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

Sputtered reactive multilayers applied as a heat source in electronic joining processes are an emerging technology. Their use promises low-stress assembly of components while improving the thermal contact and reducing the thermal resistance. Nanostructured surface modifications can significantly enhance adhesion and reliability of joints between different materials. This work examines reactive multilayer of nickel and aluminum, directly sputtered on nanostructured black silicon surfaces and compares their phase transformation with reference samples deposited on pristine silicon surface. The investigation of the quenched self-propagating reaction reveals a clear influence of the nanostructured surface on the prolongation of the phase transition. Rapid thermal annealing tests result in the formation of Al_1.1Ni_0.9 phase. The nanostructured interface seems to hinder the full transformation of the parent material. The surface modification improves the adhesion of the formed alloy on silicon surfaces and can possibly increase the reliability of joints based on reactive aluminum/nickel multilayer. The use of black silicon, a nanostructured surface modification, is thus a promising approach to realize reliable multi-material joints in complex systems.

Serious damage caused by floor heave in the coal given chamber of a vertical coal bunker is one of the challenges faced in underground coal mines. Engineering practice shows that it is more difficult to maintain the coal given chamber (CGC) than a roadway. More importantly, repairing the CGC during mining practice will pose major safety risks and reduce production. Based on the case of the serious collapse that occurred in the bearing structure of the CGC at the lower part of the 214# coal bunker in Xiashijie mine, China, this work analysed (i) the main factors influencing floor heave and (ii) the failure mechanism of the load-bearing structure in the CGC using FLAC^2D numerical models and expansion experiment. The analysis results indicate that: the floor heave, caused mainly by mine water, is the basic reason leading to the instability and repeated failure of the CGC in the 214# coal bunker. Then a new coal bunker, without building the CGC, is proposed and put into practice to replace the 214# coal bunker. The FLAC^3D software program is adopted to establish the numerical model of the wall-mounted coal bunker (WMCB), and the stability of the rock surrounding the WMCB is simulated and analysed. The results show that: (1) the rock surrounding the sandstone segment is basically stable. (2) The surrounding rock in the coal seam segment, which moves into the inside of the bunker, is the main zone of deformation for the entire rock mass surrounding the bunker. Then the surrounding rock is controlled effectively by means of high-strength bolt–cable combined supporting technology. According to the geological conditions of the WMCB, the self-bearing system, which includes (i) H-steel beams, (ii) H-steel brackets, and (iii) self-locking anchor cables, is established and serves as a substitute for the CGC to transfer the whole weight of the bunker to stable surrounding rock. The stability of the new coal bunker has been verified by field testing, and the coal mine has gained economic benefit to a value of 158.026174 million RMB over three years. The new WMCB thus made production more effective and can provide helpful references for construction of vertical bunkers under similar geological conditions.

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

As a complementary and alternative medicine in the western countries for decades, traditional Chinese medicine (TCM) has been used for more than 2000 years in China. Because of the characteristics of the philosophical style and the unknown mechanism of action, TCM sometimes has been biasedly described as "fraught with pseudoscience". From the scientific basis of the systems biology, here we promoted a novel medical model called the entropic systems medicine which could be applied to scientize TCM in future. In entropic systems medicine, TCM and Western modern biomedicine target the different variables of the entropic system. For instance, while Western modern biomedicine directly targets the phenotypes and its SOCs of macrostates, TCM differently targets the microstates, entropy and entropic force to generate SOTFs gradually causing the differentiated syndromes to be slowly rearranged. The prerequisites to modernize TCM are the entropic systems biology having been well established so that the variables could be precisely monitored and mathematically calculated.

This review paper explores the innovative realm of transparent solar panels, investigating their properties, applications, challenges, and recent advancements. Transparent solar panels, designed to harvest solar energy while allowing visible light to pass through, have gained significant attention due to their potential to revolutionize renewable energy technologies. The paper delves into the working mechanisms of various types of transparent solar panels, including Organic Photovoltaic (OPV) panels, Dye-Sensitized Solar Cells (DSSC), Perovskite Solar Cells, and Transparent Thin-Film Silicon Solar Cells. It discusses their integration into diverse applications, such as building-integrated photovoltaics, consumer electronics, the automotive industry, and aerospace. The review identifies challenges, including low efficiency, durability concerns, production costs, and integration hurdles, that currently hinder their widespread adoption. Moreover, the paper emphasizes the transformative potential of transparent solar panels in shaping the future of renewable energy, not only as energy generators but also as aesthetically pleasing and multifunctional components of our built environment. Finally, the paper underscores the need for continued research and development efforts to enhance efficiency, improve stability, and address integration challenges, paving the way for transparent solar panels to play a central role in the transition toward a more sustainable and energy-efficient future.

The effect of Localized Surface Plasmon (LSP) of 5 nm mean size Au particles deposited on TiO2 P25 was investigated during the photo-thermal water gas shift reaction (WGSR). The effects of CO concentration, excitation light flux and energy, and molecular oxygen addition during the reaction were investigated. The photocatalytic WGSR rate under light excitation with wavelengths extending from 320 to 1100 nm was found to be higher than the thermal reaction alone at the same temperature (85 oC). A ratio H2/CO2 of near unity was found at high concentrations of CO. Addition of molecular oxygen during the reaction resulted in a slight decrease of molecular hydrogen production while the rates of CO2 formation and that of CO consumption changed by one order of magnitude. More important, it was found that the WGSR rates were still high under only visible light excitation (600 - 700 nm). The results prove that Au LSP alone triggers this chemical reaction without the need to excite the semiconductor on which they are deposited.

The process of anaerobic digestion used for methane production can be enhanced by incorporating stimulating materials. The effects of these materials are dependent on various factors including the processed substrate, process conditions, and the type and amount of the stimulating material used. As part of the study, three different stimulating materials - iron powder, lime, and milled porous ceramic - were added to the 30-day anaerobic digestion of the brewer's spent grain to improve its performance. Different doses ranging from 0.2 to 2.3 gTS×L-1 were tested, and methane production kinetics were determined using the first-order model. The results showed that the methane yield ranged from 281.4±8.0 to 326.1±9.3 ml×gVS-1, while substrate biodegradation ranged from 56.0±1.6 to 68.1±0.7%. The addition of lime reduced methane yield at almost all doses by -6.7% to -3.3%, while the addition of iron powder increased methane yield from 0.8% to 9.8%. The addition of ceramic powder resulted in a methane yield change ranging from -2.6% to 4.6%. These findings suggest that the use of stimulating materials should be approached with caution, as even slight changes in the amount used can impact methane production.

Knowledge about pharmacological benefits of different seeds is an important factor for cultivation and application of medicinal herbs and plants. The seeds of medicinal plants are stores of valuable and active secondary metabolites that have been commercially and economically beneficial and helpful for medicine and pharmacy. The major parameter of reproduction and preservation of plants are also seeds which have a functional role in the distribution and establishment of plant in different regions. Five important seeds which have tremendous medicinal and pharmacological benefits are anise, basil, borage, cilantro and chamomile. Anise seed is used as a spice, either whole or ground, and its essential oil and extract are also obtained from the seeds. Basil seeds have a long history of usage in Chinese and Ayurvedic medicine, which are the good source of minerals, high in fiber including pectin, rich is flavonoids and other polyphenols. Borage seed oil is used for skin disorders such as seborrheic dermatitis, atopic dermatitis, and neurodermatitis. Cilantro is an annual herb that is part of the Apiaceae family. The seeds are rich in iron, zinc, copper and essential minerals which can decrease bad cholesterol and improves good cholesterol in the body. Chamomile can be considered for treatment of insomnia, hemorrhoids, anxiety, diarrhea and may help with wound healing and skin irritation. The keyword searches for Anise, Seed, Basil, Borage, Cilantro, Chamomile, Seed biology, Traditional medicinal science and seed anatomy were performed by using Scopus, Web of Science, PubMed and Google scholar. The aim of this article review is to survey the pharmacological and health benefits of seeds of five important medicinal plants.

The European Union Green Deal aims at curbing planet-warming greenhouse gas emissions and introducing clean energy production. But to achieve energy efficiency, the opportunity cost of different energies must be assessed. In this article, we analyse two different systems for the treatment of wastewater that, at the same time, produce energy for its own operation. On the one hand, high-rate algal ponds system (HRAP) is considered; on the other hand, we study a conventional activated sludge system which uses photovoltaic power (AS+PV). This paper offers a viability analysis of both systems based on the capacity to satisfy their energetic consumption. In order to assess this viability, the probability of not achieving the energy consumption threshold at least one day is studied. The results point that the AS+PV system self-sufficiency is achieved using much lesser surfaces than those of HRAP system (for the former, less than 6.500 m2, for the latter 40.000 m2). However, the important AS capital cost makes still the HRAP system more economic, although storage provides a great advantage for using the AS+PV in locations where we have a lot of irradiance. This viability analysis, along with the opportunity cost analysis, will be used to assess these two projects devoted to the treatment of wastewater.

Planktonic foraminiferal biomineralization intensity, reflected by their shell calcite mass, affects global carbonate deposition and is known to follow the climate cycles by being increased during glacial stages and decreased during interglacial ones. Here we measure the dissolution state and the mass of the shells of the planktonic foraminifera species Globigerina bulloides from a Tropical Eastern North Atlantic site over the last two glacial-interglacial climatic transitions and we report no major changes in plankton calcite production with the atmospheric pCO2 variations. We attribute this consistency in foraminifera calcification to the climatic and hydrological stability of the tropical regions. We however recorded increased shell masses midway through the penultimate deglaciation (Termination II). In order to elucidate the cause of the increased shell weights we performed δ18O, Mg/Ca and μCT measurements on the same shells from a number of samples surrounding this event. We find that shells of increased mass are internally contaminated by sediment infilling and that shell weights are responding to local hydrographic changes.

In article questions of development low-waste technologies of processing of steel-smelting slag are considered, gland allowing by extraction and its connections from steel-smelting slag to receive additional raw materials for production became, and the remains to use in building industry. Studying of gravitational methods of enrichment of steel-smelting slag and heat treatment the ore-fuel of pellets is the basis for work. Proceeding from it, in work modern physic-mechanical, chemical and physical and chemical methods of researches (UV-spectroscopy, electronic microscopy, the granulometric analysis) are used.

We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. We review large deviations techniques useful in this context to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.

Interest in leadership skills and leadership development is burgeoning both in academic circles and business organizations. Leadership research has produced a large number of leadership models. The distinctions between leadership and management, skills required for leadership, and methods of leadership development are ongoing areas of study. An attempt is made to provide an overarching theory that showcases some crucial distinctions. The proposed theory holds that ‘managing’ is about improving a situation, and ‘leading’ is about improving a situation by stretching oneself either by exploiting unique personal characteristics or by exploring underdeveloped skills and/ or enabling others and organizations to do the same. Thus, leadership is an act of self-expansion, and everyone can lead, including followers. Leadership development requires a series of acts of leading and learning, leading to leadership positions in a Darwinian process. Thus, at the heart of leadership is experimentation, and hence, it can be rightly called the art of management. What leaders do successfully today becomes management science tomorrow. The ever-growing list of leadership skills and leadership theories with more prosocial views is evidence of leadership in theory and practice. We also propose holistic leadership as a new type of leadership.

Climate change poses a significant threat to European security, implicitly to Romania’s naval forces in the Black Sea. Rising sea levels, extreme weather events, and ocean acidification impact maritime operations, infrastructure, and personnel, with potential ramifications for regional stability. This paper explores the implications of climate change for the Romanian Navy and proposes "Green Defence" strategies to enhance resilience and sustainability. These strategies encompass energy efficiency, renewable energy integration, sustainable procurement, and the application of advanced functional materials in naval operations and infrastructure. By adopting a proactive and multifaceted approach, the Romanian Navy can adapt to the changing climate, ensuring operational effectiveness while minimizing environmental impact. This approach aligns with broader European security concerns and contributes to a more sustainable and resilient defence posture in the face of climate-related challenges.

Differential Evolution (DE) is an evolutionary algorithm that has become increasingly popular due to its effectiveness and efficiency. However, its adaptation for multi-objective optimization requires further investigation. To address this issue, a novel algorithm called ILSDEMO has been proposed, which incorporates indicator-based selection and local search into a self-adaptive DE. ILSDEMO employs an archive population to store non-dominated solutions, generating an initial population uniformly distributed over the feasible solution space using orthogonal design. Additionally, two variants of DE are used to expedite convergence, while q-Gaussian mutation is utilized to exploit better trial individuals. The k-nearest neighbor rule is used to eliminate crowded solutions, and indicator-based selection is employed to generate a new parent population without diversity preservation. The performance of ILSDEMO was investigated on the test instances from the ZDT series and DTLZ series in terms of the selected indicators. The results suggest that ILSDEMO accurately and evenly approximates the true Pareto front compared to NSGAII, IBEA, and DEMO.

This study considers ways to increase the consumption of sustainable fashion, given the significant environmental and social damages, associated with the industry. A series of experiments were conducted examining the impacts of choice architecture (nudges) under field conditions in collab-oration with one of Israel’s largest shopping centers. The study sought to identify which inter-ventions at the retail level successfully motivate sustainable fashion behavioral change regarding purchases and willingness to pay more, along with agreement with several statements regarding the climate crisis and sustainable fashion. Among the types of nudges examined in this field study were providing information, increasing accessibility to sustainable alternatives and appealing to social identity, in relation to demographics and green self-image. The study found that offering alterna-tives to consumers constitutes the most effective way to “nudge” consumers toward more sus-tainable purchasing behavior. Nonetheless, this does not negate the contribution of providing in-formation and strengthening social norms regarding sustainable fashion. Additionally in all groups, most participants reported that they do not know how to distinguish between sustainable and non-sustainable fashion, nor do they believe that the clothes they purchased were actually sustainable. The findings emphasize the need for policies that will increase the accessibility of sustainable fashion.

An innovative concrete that incorporates the advantages of self-compacting concrete with geopolymer is called self-compacting geopolymer concrete. The objective of this study was the Manufacturing of M30-grade self-compacting geopolymer concrete by adding industrial materials like fly ash (FA), ultrafine ground granulated blast furnace slag (UFGGBS), and silica fume (SF) as minerals. The disposal of these industrial wastes can be substantially overcome, as they lead to land pollution. The fixed powder content was 500 kg/m³. A mixture of sodium silicate (Na₂SiO₃) and sodium hydroxide (NaOH) created an alkaline solution that activated the cementitious binders. In this study, slump flow, L-box, V Funnel, T50 V Funnel, and J ring tests were carried out to determine the fresh qualities of the concrete. These tests were conducted under the regulations for self-compacting concrete provided by EFNARC (2005). To improve the flexural strength of the SCGC, 1.5 % glass fibre was added. This study illustrates how an artificial neural network (ANN) model can determine the concrete mix proportion. The characteristics of SCGC, both fresh and hardened properties test were investigated for molarities of 8, 10, and 12. The microstructural outcomes were determined using Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and X-ray diffraction (XRD).

The prolonged static sitting at the workplace is considered as one of the main risks for development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar hyperlordosis and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. This publication proposes the low back pain assessment (LBPA) dataset collected through experiment with 100 participants and consisting of photogrammetric images with intentionally placed body markers, calculated postural angles and tags correct and incorrect posture assessed by habilitated rehabilitator, as well as questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed for recognizing incorrect body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms.

The aim of this randomized controlled clinical trial was to compare the 2-year clinical performance of resin composite restorations placed at non-caries cervical lesions (NCCL) with one-step self-etch, total-etch, and selective enamel etch & self-etch adhesive techniques. Thirty-two patients received three resin composite restorations each at NCCLs (Tetric EvoCeram/Ivoclar/Vivadent), bonded with a total-etch adhesive agent (ExciTE F/Ivoclar/Vivadent) and a self-etch (AdheSE One F/Ivoclar/Vivadent) without and with selective enamel etching. All restorations were evaluated by two examiners at baseline, 6-, 12-, 18- and 24-months post-operation regarding retention, caries occurrence, marginal adaptation, and marginal staining. A logistic regression analysis with generalized estimating equations was used. After 2 years, the retention rate was 86,8% for total-etch, 92.26% for self-etch, and 93.63% for selective enamel etching & self-etch. No caries were detected during all the restorations. Concerning marginal adaptation, the clinically perfect restorations were 26.9% for the total-etch technique, 16% for self-etch, and 25,9% for selective enamel etch & self-etch. All three adhesive strategies provided restorations with no significant differences in the retention rate and the marginal adaptation, whereas the total etch yielded better performance for marginal staining. All the restorations were assessed as clinically acceptable after 2 years.

Aims: This study aims to evaluate the impact of family-centered empowerment on Jordanian adolescents with type 1 diabetes (T1DM), focusing on their HbA1c levels, self-efficacy, and quality of life (QOL). Background: Poor glycemic management in adolescents with type 1 diabetes increases complications. Improved control is linked to various variables, but few studies focus on Middle Eastern populations. Methods: An experimental study involved 68 adolescents with T1DM visiting Jordanian Royal Medical Services' clinics. Two sets of participants were created: control (n = 34) and intervention (n = 34). Data was collected through face-to-face interviews and medical records. Results: The study found significant differences in self-efficacy scores between the intervention and control groups, with the intervention group experiencing a significant decrease in HbA1c readings and a significant difference in QOL between pre-program and post-program phases. Discussion: Family empowerment can significantly improve the lives of Jordanian teenagers with type 1 diabetes. This strategy leads to improved glycemic management, increased self-efficacy, and improved quality of life. Future treatments and health policies should recognize and value the critical role families play in the care of this population. Conclusion: The study suggests that patients with T1DM should receive continuous care education sessions, including self-care training, to improve their health. Nurses should also incorporate these training into treatment plans and educational programs for adolescents to enhance their QOL.

Abstract: Fetal Alcohol Spectrum Disorder (FASD) is considered a lifelong disability that has been framed by neurobiological descriptions focused on the brain. These are important features but fail to tell the story of living with FASD. By surveying those with FASD, this work expands upon prior survey work which illustrated a multitude of early onset physiological issues occurring at rates much higher than typical of the general population. The current project, again using an anonymous survey methodology, seeks to open up other direct experiences to better understand the complexity of living with FASD. An anonymous online survey was used to gather data on advresity in childhood, schooling, employment, housing and finances, involvement with the criminal justice system as well as relationships and parenting. Results inform how supports can be enhanced and targeted with a goal of improving the quality of life. The survey was developed by adults living with FASD who have served as a long-standing advocacy and educational group influencing policy and practice in the field.

We explore 3D printing modules for self-assembling spacecraft and robots – structure, actuators, electrics. Self-assembly has long been viewed as a highly desirable capability for autonomous construction of large space structures. We review self-assembly in space which focusses on the self-assembly of modules that demonstrate the range of applications. However, self-assembly may be synergised with 3D printing to offer an automated capability from raw material to modules for assembling new spacecraft or habitats. One application of 3D printing is using space debris on-orbit as an in-situ resource - defunct spacecraft may be salvaged as raw material for in-situ construction on demand. The common features of all self-assembling modules are that the modules constitute a structure housing a computer-controlled actuator internally and a reversible latching mechanism externally. We have demonstrated a 3D printed DC electric motor in which the only components that were not 3D printed are the wire coils. We have married our 3D printed motor prototype as an actuated joint between two 3D printed trigon-type panels developed as part of a trigon self-assembling system concept. The trigon concept underlies a modular approach to self-assembling and self-deploying structures. The 3D printed motorised panel system demonstrates that the motor aspect and structural aspects of robotic self-assembling machines are amenable to 3D printing. This has implications for self-assembling systems into modular satellites as a solution to space debris.

With the development of artificial intelligence, the ability of capturing the background characteristics of hyperspectral imagery (HSI) is improved and promising performance in hyperspectral anomaly detection (HAD) tasks is yielded. However, existing methods proposed in recent years still suffer from certain limitations: 1) constraints are lacking in the deep feature learning process in terms of the issue for absence of prior background and anomaly information. 2) hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. 3) architectures of fully connected deep network in hyperspectral anomaly detectors lead to low utilization of spatial information and destruction for original spatial relationship of hyperspectral imagery, and disregard spectral correlation between adjacent pixels. 4) hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. With the consideration of above problems, in this paper, we propose a novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency (FCAE-DCAC) for HAD which is carried out in a self-supervised learning-based processing. Firstly, the density-based spatial clustering of applications with noise algorithm and connected component analysis are utilized for successive spectral and spatial clustering to obtain more precise prior background and anomaly information, which facilitates the separation between background and anomaly samples during the training of our method. Subsequently, a novel fully convolutional auto-encoder (FCAE) integrated with spatial-spectral joint attention (SSJA) is proposed to enhance the utilization of spatial information and augment feature expression. In addition, a latent feature adversarial consistency network is proposed to achieve pure background reconstruction with the ability of learning actual background distribution in hyperspectral imagery. Finally, a triplet loss is introduced to enhance the separability between background and anomaly, and the reconstruction residual serves as the anomaly detection result. We evaluate the proposed method on seven groups of real-world hyperspectral datasets, and the experimental results confirmed the effectiveness and superior performance of the proposed method versus nine state-of-the-art methods.

The study focuses on a novel porous TiNi-based material for ophthalmic orbital implants which was produced by self-propagating high-temperature synthesis (SHS) and, after proper surface modification, possesses a specific microstructure of its surface pores, which makes it attractive for biomedical use in implants. After preparation via SHS, the obtained porous TiNi material was etched in acidic environment, to get rid of its surface Ti2Ni secondary-phase particles. This was found to improve the material’s surface morphology, adding micro-roughness to its macro-rough pores. As a result, cell growth tests conducted on the material demonstrated improved cell adhesion and growth kinetics on such a porous material with improved roughness. Finally, the material was tested in vivo as an ophthalmic orbital implant, demonstrating good biocompatibility, good degree of biointegration with surrounding eyeball tissues, and no signs of rejection after as long as 180 days. Thus, the novel porous TiNi-based material shows promise for its use in ophthalmic implantology, for instanse for manufacturing musculoskeletal stumps of the eyeball after evisceration, as it is biocompatible, has a high tissue-implant integration potential and demonstrates reduced risks of exposure and rejection of the implant.

This work deals with a novel porous TiNi-based material for ophthalmic orbital implants that was produced by high-temperature synthesis (SHS) and possesses a specific microstructure of its surface pores, which makes it attractive for biomedical use in implants. After preparation via SHS, the obtained porous TiNi material was etched in acidic environment, to get rid of its surface Ti2Ni secondary-phase particles. This was found to improve the material’s surface morphology, adding micro-roughness to its macro-rough pores. As a result, cell growth tests conducted on the material demonstrated improved cell adhesion and growth kinetics on such a porous material with improved roughness. Finally, the material was tested in vivo as an ophthalmic orbital implant, demonstrating good biocompatibility, good degree of biointegration with surrounding eyeball tissues, and no signs of rejection after as long as 180 days. Thus, the novel porous TiNi-based material shows promise for its use in ophthalmic implantology, for instanse for manufacturing musculoskeletal stumps of the eyeball after evisceration, as it is biocompatible, has a high tissue-implant integration potential and demonstrates reduced risks of exposure and rejection of the implant.

In an outstanding article published in 2008, Suzuki obtained a nice generalization of the Banach contraction principle from which derived a characterization of metric completeness. Although Suzuki’s theorem has been successfully generalized and extended in several directions and contexts, we here show by means of a simple example that the problem of achieving, in an obvious way, its full extension to the framework of w-distances does not have an emphatic response. Motivated by this fact we introduce the concept of presymmetric w-distance on metric spaces, we give some properties and examples of this new structure and show that it provides a reasonable setting to obtain a real and hardly forced w-distance generalization of Suzuki’s theorem. This is realized in our main result, which is a fixed point theorem that involves presymmetric w-distances and certain contractions of Suzuki-type. We also discuss the relationship between our main result and the well-known w-distance full generalization of the Banach contraction principle, due to Suzuki and Takahashi. Connected to this approach we prove another fixed point result that compare with our main result through some examples. Finally, we state a characterization of metric completeness by using our fixed point results.

In today’s competitive landscape, achieving customer-centricity is paramount for the sustainable growth and success of organisations. This research is dedicated to understanding customer preferences in the context of the Internet of Things (IoT) and employs a two-part modeling ap-proach tailored in this digital era. In the first phase, we leverage the power of the Self-Organizing Map (SOM) algorithm to segment IoT customers based on their connected device usage patterns. This segmentation approach reveals three distinct customer clusters, with the second cluster demonstrating the highest propensity for IoT device adoption and usage. In the second phase, we introduce a robust Decision Tree methodology designed to prioritize various factors influencing customer satisfaction in the IoT ecosystem. We employ the Classification and Regression Tree (CART) technique to analyze 17 key questions that assess the significance of factors impacting IoT device purchase decisions. By aligning these factors with the identified IoT customer clusters, we gain profound insights into customer behaviour and preferences in the rapidly evolving world of connected devices. This comprehensive analysis delves into the factors contributing to customer retention in the IoT space, with a strong emphasis on crafting logical marketing strategies, en-hancing customer satisfaction, and fostering customer loyalty in the digital realm. Our research methodology involves surveys and questionnaires distributed to 207 IoT users, categorizing them into three distinct IoT customer groups. Leveraging analytical statistical methods, regression analysis, and IoT-specific tools and software, this study rigorously evaluate the factors influencing IoT device purchases. Importantly, this approach not only effectively clusters the IoT Customer Relationship Management (IoT-CRM) dataset but also provides valuable visualizations that are essential for understanding the complex dynamics of the IoT customer landscape. Our findings underscore the critical role of logical marketing strategies, customer satisfaction, and customer loyalty in enhancing customer retention in the IoT era. This research makes a significant contri-bution to businesses seeking to optimize their IoT -CRM strategies and capitalize on the oppor-tunities presented by the IoT ecosystem.

Premature failure and degradation of layers are the main problems for transportation infrastruc-ture. Addressing these issues necessitates implementing structural health monitoring (SHM) for transportation layers. This strategic approach is instrumental in mitigating maintenance expenses, prolonging the operational lifespan, and preventing accidents resulting from abrupt layer col-lapse. To this end, this research investigated the stress/strain and damage detection capabilities of a self-sensing cementitious composite developed for potential utilization in the construction of an intelligent subgrade layer. The prepared self-sensing cementitious composite consisted of 10% cement and hybrid conductive fillers, including multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) in sand. Initial findings reveal that the electrical resistivity of the composite is significantly affected by the concentration of MWCNTs/GNPs, with a minimum concentration of more than 0.5% needed to achieve a responsive cementitious composite. More-over, the piezoresistive analysis indicates that an increase in the concentration of MWCNTs/GNPs and stress levels leads to an improvement in the stress/strain sensing perfor-mance. When the self-sensing cementitious composite is subjected to equivalent stress levels, variations in the FCR exhibit an increasing trend with decreasing resilient modulus, stemming from a decrease in stiffness due to the increased concentration of MWCNTs/GNPs. Additionally, the EIS analysis demonstrates a contraction for the Nyquist curves under compressive ramp loading prior to failure, followed by the expansion of these curves postfailure. Scanning electron microscopy (SEM) images visually showcase the bridging effects of MWCNTs and the filling ef-fects of GNPs within the composite structure.

Block copolymers are a class of materials particularly interesting for their ability to self-assemble into ordered structures. In this context, the coupling between environment and dynamics is particularly relevant, given that movements at the molecular level influence various properties of macromolecules. Mixing the polymer with a second macromolecule appears to be an easy way to study these relationships. In this work, we studied a system based on two diblock copolymers of methyl methacrylate as first block and 3-methyl-4-[6-(methylacryloyloxy)-hexyloxy]-4’-pentyloxy azobenzene as second block and their blends with PMMA. The relaxational properties of these systems were investigated by electron spin resonance (ESR) spectroscopy sensitive to nanometric length-scales. The results of the study on the blends were related to the dynamic behavior of the copolymer. The study evidenced the presence of fast and slow molecular sites for the molecular reorientation at nanoscale, modulated by the ability of block copolymer to form supramolecular structures. For blends, the heterogeneities at nanoscale are still detected. However it was found that the presence of the PMMA as the major component of the blends modified their dynamic behaviour.

Medical Self-service Terminals (MST) offer potential for process optimization and enhanced patient convenience in hospitals, including the elderly. However, the behavioral inclination (BI) of elderly Chinese individuals towards MST adoption remains suboptimal. This study aims to elucidate BI determinants for MST among the aging demographic, focusing on User Interface (UI) attributes within the extended Unified Technology Acceptance and Use Theory (UTAUT) Model. The empirical inquiry examines seven factors, comprising three key UI attributes and four fundamental UTAUT factors. Results reveal significant positive associations of Performance Expectancy (β=0.40, p<0.001), Effort Expectancy (β=0.50, p<0.001), and Social Influence (β=0.25, p<0.05) with BI. Notably, User Interface Design positively correlates with Performance Expectancy (β=0.89, p<0.001) and Effort Expectancy (β=0.81, p<0.001). These findings emphasize the interplay between objective UI attributes and subjective UTAUT determinants. This research enhances understanding of UI's impact on MST BI and acceptance, particularly within the Chinese elderly population, urging MST researchers to prioritize elderly attitudes for effective technological integration in healthcare.

Life in the modern century is heavily reliant on an enormous amount of electricity consumption as technology has become the most integral part of daily life. In this context, smart grid systems play a pivotal role to maintain the uninterrupted power supply which needs to be monitored in a timely fashion to keep track of the electric consumers’ usage pattern. The smart meter is the one of smart applications of the smart grid that collects huge amounts of consumer load data on a daily basis which has become a focus for various researchers and analyzers to study load characterization. In this paper, an approach has been proposed to recognize the energy consumption patterns among diverse types of consumers ranging from residential to industrial levels. This approach is worth considering not only for load pattern recognition but also for involving customers in different events such as demand response or peak shaving. In such a way, this analytical mechanism certainly assists in reducing power wastage and saving costs. The proposed methodology is based on a two-fold clustering algorithm with the use of state-of-the-art technology, machine learning. The primary goal is to classify electric customers' data collected from smart meters. Then, analyzing the classified results with an aim to predict power consumption patterns for the customers in the future and making the right energy policy that will benefit both the grid operator and consumers as well.

Attention deficit hyperactivity disorder (ADHD) results in various functioning impairments in children’s lives and families. Parents of children with ADHD report high levels of parenting stress, low levels of parental self-efficacy and use of more authoritarian and/or permissive parenting practices than parents of typically developing children. Intervention programs need to address both children’s and parents’ needs and multimodal intervention programs could cover this demand. The aim of this study was to examine the efficacy of “Child ViReal Support Program” - a multi-level evidence-based comprehensive program - on parenting stress, parental self-efficacy, parenting practices, and the core symptoms of children’s ADHD. Families with a child diagnosed with ADHD (n = 16) were randomly allocated to two groups (PC and CP; P = parent training, C = child training) and a cross-over design was utilized. Participating parents completed, in four different times during the study, the Parenting Stress Index-Short Form, the Parenting Sense of Competence Scale, the Parenting Styles and Dimensions Questionnaire-Short Version and the parent form of the ADHD Rating Scale-IV. Parents from both groups, after their participation in the parent training, demonstrated reduced parenting stress, enhanced parental self-efficacy and increased the employ of democratic parenting practices. More than that, they reported decreased levels of inattention and impulsivity/hyperactivity for their children. Evidence-based multi-level intervention programs could produce positive effects in parents and children by incorporating effective methods and tools in accordance with the needs and the demands of the family context.

Abstract: Hox gene clusters are crucial in Embryogenesis. It was observed that some Hox genes were located in order along the telomeric to centromeric direction of the DNA sequence: Hox1, Hox2, Hox3…. These genes were expressed in the same order in the ontogenetic units of the Drosophila embryo along the Anterior-Posterior axis. The two entities (genome and embryo) differ significantly in linear size and in-between distance. This strange phenomenon was named Spatial Collinearity (SP). Later, it was observed that, particularly in the Vertebrates, a Temporal Collinearity (TC) coexists: first is Hox1 expressed, later Hox2 and even later Hox3,…,. Hox clusters are irreversibly elongated along the force direction. According to a Biophysical Model (BM), pulling forces act at the anterior end of the cluster while a cluster fastening applies at the posterior end. During Evolution, the elongated Hox clusters are broken at variable lengths thus split clusters may be created. An Empirical Rule was formulated distinguishing development due to a complete Hox cluster from development due to split Hox clusters. BM can explain this Empirical Rule. In an accidental mutation where the cluster fastening is dismantled, a minimal pulling force can automatically shift the cluster inside the Hox activation domain. This cluster translocation can probably explain the absence of temporal collinearity in Drosophila.

The practice of quantified-self sleep tracking is increasingly common nowadays among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization, and are incapable of providing actionable recommendations that are tailored to users' physical, behavioral and environmental context. Here we coined the term context-aware sleep health recommender system (CASHRS) as an emerging multidisciplinary research field that bridges ubiquitous sleep computing and context-aware recommender systems. In this paper, we presented a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges in peer-reviewed publications that meet the characteristics of CASHRS. Analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS.

The article continues the author's publications about the matrix-tensor study of universal rules of stochastic (probabilistic) organization of long single-stranded DNA sequences in eukaryotic and prokaryotic genomes. The author reveals that corresponding matrices of probabilities of n-plets in n-textual representations of each genomic DNA are numerically interrelated each with other in such algebraic form, which has analogies with formalisms of the known tensor-matrix theory of digital antenna arrays. These arrays combine many separate antennas into a single coordinated ensemble with unique emergent properties, due to which antenna arrays are widely used in devices of medicine, astrophysics, avionics, etc. The noted analogies allow putting forward the author's hypothesis that stochastic organization of genomic DNAs is connected with bio-antenna arrays. From the point of view of this hypothesis, many known facts about using principles of antenna arrays in inherited physiological phenomena are collected in a single grouping with genomic DNA: echolocation of dolphins and other animals; complex faceted eyes of insects; images on butterfly wings, consisting of many photonic crystals as nanoantenna arrays, etc. This new topic about the biological meaning of profitable properties of antenna arrays includes problems of biological evolution, the origin of the genetic code, regenerative medicine, development of algebraic biology, which are discussed.

Background / Objective: Historically, fasting has not only been practiced for medical but also for religious reasons. Bahá’ís follow an annual religious fast of 19 days. We assessed motivation behind and subjective health impacts of Bahá’í fasting. Methods: A convergent parallel mixed methods design was embedded in a clinical single arm observational study. Semi-structured interviews were conducted with six fasters before, during and after fasting. Three months after the fasting period, two focus group interviews were conducted. 146 Bahá’í volunteers answered an online survey at five time points before, during and after fasting. Results: Interviews: Fasting was found to play a central role for the religiosity of interviewees, implying changes in daily structures, spending time alone, engaging in religious practices, and experiencing social belonging. Results show an increase in mindfulness and well-being, accompanied by behavioural changes and experiences of self-efficacy and inner freedom. Survey: Scores point to an increase in mindfulness and well-being during fasting, while stress, anxiety and fatigue decreased. Mindfulness remained elevated even three months after the fast. Conclusion: Bahá'í intermittent dry fast seems to enhance participants’ mindfulness and well-being, lowering stress levels and reducing fatigue. Some of these effects lasted more than three months after fasting.

The structure of self-reinforced composites (SRCs) based on ultra-high molecular weight 21 polyethylene (UHMWPE) was studied by means of Wide-Angle X-Ray Scattering (WAXS), X-Ray 22 tomography, Raman spectroscopy, Scanning Electron Microscopy (SEM) and in situ tensile testing 23 in combination with advanced processing tools like Avizo, ImageJ, and Ncorr to determine the cor-24 relation between the processing conditions, on the one hand, and the molecular structure and 25 mechanical properties, on the other. SRCs were fabricated by hot compaction of UHMWPE fibers at 26 different pressure and temperature combinations without addition of polymer matrix or softener. 27 It was found by WAXS that higher compaction temperatures led to more extensive melting of 28 fibers with the corresponding reduction of the Herman’s factor reflecting the degree of molecular 29 orientation, while the increase of hot compaction pressure suppressed the melting of fibers within 30 SRCs at a given temperature. X-Ray tomography proved the absence of porosity while polarized 31 light Raman spectroscopy measurements for both longitudinal and perpendicular fiber orienta-32 tions showed qualitatively the anisotropy of SRC samples. SEM revealed that the matrix was 33 formed by interlayers of molten polymer entrapped between fibers in SRCs. Moreover, in situ 34 tensile tests demonstrated the increase of Young’s modulus and tensile strength with increasing 35 temperature.

Biological technologies are fundamentally unlike any other because biology evolves. Bioengineering therefore requires novel design methodologies with evolution at their core. Knowledge about evolution is currently applied to the design of biosystems ad hoc. Unless we have a unified engineering theory of evolution, we will neither be able to meet evolution’s potential as a design tool, nor understand or limit its unintended consequences on our designs. Our concept of the evotype offers a conceptual framework for engineering the evolutionary potential of biosystems. We show how a biosystem’s evolutionary properties might be rationally designed by engineering aspects of genetic variation, designed function, and natural selection. This idea could apply to all biosystems – from individual proteins to communities of whole-cells or even entire ecosystems – whether the goal is to direct evolution in the design process, or to limit its impacts during application. These principles could even be used beyond the realm of bioengineering to design entirely synthetic evolving auto-adaptive technologies.

In today's world it is very much important to maintain the security of information and its risks. The biometric-based techniques are very much useful in these problems. Among the several kinds of biometric-based technique, face detection is much complex and much more important. Due to the age and several other problems, a human face structure changes over time, again a human has lots of expressions. Sometimes due to the lighting condition or the variation of the angle of an input device, the pattern of a human face structure also changed. As a result, the face cannot be detected properly. In this paper, a method is proposed that can detect the human faces both automatically and manually very efficiently. In manual mode, a user can select the input faces referred by the system according to their choice. In automated mode, the system detected all possible face areas using the Kohonen Self-Organizing Feature Map technique. This method reduced the complex color image into a vector quantized image with desired colors. Then a color segmentation technique is used to detect the possible face skin areas from the vector quantized image. Then the Histogram Oriented Gradient technique used to detect the feature from the faces and K-Nearest Neighbour Classifier is used to compare both face images detected by the two modes. The automated method prosed better accuracy than the manual method.

Recently, several research groups have reported on anomalous enhancement of the self-field critical currents, Ic(sf,T), at low temperatures in superconductor/Dirac-cone material/superconductor (S/DCM/S) junctions. Some papers attributed the enhancement to the low-energy Andreev bound states arising from winding of the electronic wave function around DCM. In this paper, Ic(sf,T) in S/DCM/S junctions have been analyzed by two approaches: modified Ambegaokar-Baratoff and ballistic Titov-Beenakker models. It is shown that the ballistic model is an inadequate tool to analyze experimental data from S/DCM/S junctions. The primary mechanism for limiting superconducting current in S/DCM/S junctions is different from the conventional view that the latter is the maximum value within the order parameter phase variation. Thus, there is a need to develop a new model for self-field critical currents in S/DCM/S systems.

In this paper, we study a generalised CIR process with externally-exciting and self-exciting jumps, and focus on the distributional properties and applications of this process and its aggregated process. The first and second moments of this jump-diffusion process are used to calculate the insurance premium based on mean-variance principle. The Laplace transform of the aggregated process is derived, and this leads to an application for pricing default-free bonds which could capture the impacts of both exogenous and endogenous shocks. Illustrative numerical examples and comparisons with other models are also provided.

In emergency scenarios service vehicles must identify potential route(s) and use the best available route. However, route identification requires intelligent decision-support systems which generally use non-traditional approaches with tools characterised by flexible non-hierarchical structures. Conventional models using group decision-support systems have been applied; however, when used in smart urban environments, emergency response services have limitations in their ability to identify unobstructed paths (routes) in dynamic operating environments. In this paper we introduce a novel path planning method for autonomous vehicle control in emergency situations. The proposed model uses self-organizing maps in an integrated Spiral STC algorithm termed the: Hybrid SOM-Spiral STC model which uses hedge algebras and \emph{Kansei} evaluation in group decision-support. The proposed model has been designed to quantify qualitative factors using sensor derived data processed with human sensibilities and preferences in emergency decision support. The experimental results show that the proposed model achieves significant improvements in group decision-support under dynamic uncertainty. We posit that our novel approach holds the prospect of improvements in the provision of emergency services.

The contribution of extra-tropical synoptic cyclones to the formation of summer-mean atmospheric circulation patterns in the Arctic is investigated by clustering the dominant Arctic circulation patterns by the self-organizing maps (SOMs) using the daily mean sea level pressure (MSLP) in the Arctic domain (≥ 60°N). Three SOM patterns are identified: one with prevalent low pressure anomalies in the Arctic Circle (SOM1) and two opposite dipoles with primary high pressure anomalies covering the Arctic Ocean (SOM2 and SOM3). The time series of summertime occurrence frequencies demonstrate the largest inter-annual variation in the SOM1, the slight decreasing trend in the SOM2, and the abrupt upswing after 2007 in the SOM3. The relevant analyses with produced cyclone track data confirm that the vital contribution. The Arctic cyclone activity is enhanced in the SOM1 because the meridional temperature gradient increases over the land–Arctic Ocean boundaries co-located with major extra-tropical cyclone pathways. The composite daily synoptic evolutions for each SOM reveal that the persistence of all the three SOMs is less than 5 days on average. These evolutionary short-term weather patterns have substantial variability at inter-annual and longer timescales. Therefore, the synoptic-scale activity is central to forming the seasonal-mean climate of the Arctic.

Ovarian Cancer (OC) is a type of cancer that affects ovaries in women, and is difficult to detect at initial stage due to which it remains as one of the leading causes of cancer death. The ovarian cancer data generated from the Internet of Medical Things (IoMT) was used and a novel approach was proposed for distinguishing the ovarian cancer by utilizing Self Organizing Maps (SOM) and Optimal Recurrent Neural Networks (ORNN). SOM algorithm was utilized for better feature subset selection and was also utilized for separating profitable, understood and intriguing data from huge measures of medical data. In supervised learning techniques, the SOM-based feature selection seems to be a tougher challenge because of the absence of class labels that would guide the search for relevant information to the classifier model. The classification approach can identify ovarian cancer data as benign/malignant. The ovarian cancer detection process can be improved by optimizing the weights of RNN structure using Adaptive Harmony Search Optimization (AHSO). The proposed model in this study can be used to detect cancer at early stages with high accuracy and low Root Mean Square Error (RMSE).

The diagnosis of Cardio-Vascular diseases (CVD) is highly dependent on analysis of ECG signals. ECG analysis can be helpful in estimating the underlying cause and condition of heart in cardiac abnormality. The effectiveness of ECG signal analysis in detection of CVDs is widely accepted by professional healthcare service provider. Many algorithms have been proposed but almost all of them have some kind of limitations, and these limitations largely influence the effectiveness of ECG analysis. The performed research work is dedicated for design of unique self-organizing maps (SOMs) based neural network for classification of arrhythmia according to a particular ECG signal, the generation of SOMs is based on the certain unique signatures of ECG signals and have potential to classify different cardiac conditions. For extraction of unique features from ECG signals, we have proposed to use Fast Discrete Stockwell Transform (FDST). Since the proposed technique is a result of combining two different techniques hence called as hybrid technology. The purpose of using FDST is to identify unique signatures of ECG signals in a more improved manner than existing one, the term improved is used because it has several advantages over existing techniques such as wavelet and Fourier Transform based methods. Results obtained from the implementation of the technique are capable in visualizing the ECG sinus rhythm and arrhythmia conditions in form of unique SOM for each associated arrhythmia condition. This unique SOM based classification makes them ideal for being used as a diagnostic tool. This ability of arrhythmia classification using FDST and SOMs makes the technique unique and useful providing valuable information about patient condition. Using proposed technology a portable diagnosis tool for monitoring of patient at their site may be facilitated later, that will improve the quality of life of the patient by diagnosing cardiac condition.

Fluid flow in human body is generally known to influence a variety of cellular behaviors. Different nanoparticle properties as well as cell type, interaction with other cells and cellular environments also show significant effect on nanoparticle uptake and drug efficacy. The aim of this study was to evaluate the effect of shear stress on cellular behaviors of biocompatible and biodegradable nanoparticles to cancer cells (A549 cell lines) in a biomimetic microfluidic system. We prepared a gelatin-oleic conjugate (GOC) as an amphiphilic biomaterial to prepare self-assembled gelatin-oleic nanoparticles (GON). Coumarin-6 and paclitaxel were used as the fluorescence marker and model drug, respectively, and were loaded into GONs by incubation (C-GONs; PTX-GONs). Additionally, we evaluated the cellular uptake of fluorescence labeled C-GONs and the drug efficacy of PTX-GONs. The cellular uptake of C-GONs by A549 cells in the absence of shear stress revealed that the mean fluorescence intensity was slightly decreased compared to that in the presence of shear stress. The results also indicated that negatively charged PTX-GONs had a lower cancer killing effect under dynamic conditions than that under static conditions. It also suggested that fluidic shear stress did not significantly affect drug uptake and efficiency in case of PTX-GONs. The cellular interactions between nanoparticles and cells in drug delivery should be carefully examined according to the physicochemical properties of nanoparticles such as the type of materials, size and mainly surface charge in a biomimetic microfluidic condition.

An energy production system consisting solar collector, biogas dry reforming reactor and solid oxide fuel cell (SOFC) was assumed to be installed in Kolkata, India. This study aims to understand the impact of climate condition on the performance of solar collectors with different lengths of parabolic trough solar collector (dx) and mass flow rate of heat transfer fluid (m). In addition, this study has evaluated the amount of H2 produced by biogas dry reforming (GH2), the amount of power generated by SOFC (PSOFC) and the maximum possible households (N) whose electricity demand could be met by the energy system proposed, considering the performance of solar collector with the different dx and m. As a result, the optimum dx was found to be 4 m. This study revealed that temperature of heat transfer fluid (Tfb) decreased with the increase in m. Tfb in March, April and May was higher than that in other months, while Tfb from June to December was the lowest. GH2, PSOFC and N in March, April and May were higher than that in other months irrespective of m. The optimum m was 0.030 kg/s.

Research with deuterium-depleted water (DDW) in the last two decades proved that deuterium/hydrogen ratio has a key role in cell cycle regulation and cellular metabolism. The present study aimed to investigate the possible effect of deuterium-depleted organic compounds (DDOC) alone and in combination with DDW on cancer growth in two in vivo mouse models. To produce DDOC, drinking water of laying hens was replaced with DDW (25 ppm) for 6 weeks, resulting in 60 ppm D level in dried egg yolk that was used as deuterium-depleted food additive. In one model, 4T1, a cell line with high metastatic capacity to the lung, was inoculated in the mice’s mammary pad. After three weeks of treatment with DDW and/or DDOC, the tumor volume in the lungs was smaller in all treated groups vs. controls with natural D level. Tumor growth and survival in mice transplanted with MCF-7 breast cancer cell line showed that the anticancer effect of DDW was enhanced by food containing the deuterium-depleted yolk. The study confirmed the importance of D/H ratio not only in consumed water but also in metabolic water produced by the mitochondria while oxidizing nutrient molecules. This is in line with the concept that initiation of cell growth requires the cells to generate a higher D/H ratio, but DDW, DDOC, or the naturally low-D lipids in a ketogenic diet, have significant effect on tumor growth by preventing the cells from raising D/H ratio to the threshold.

Environmental impact associated with odor and gaseous emissions from animal manure is one of the challenges for communities, farmers, and regulatory agencies. Microbe-based manure additives treatments are marketed and used by farmers for mitigation of emissions. However, their performance is difficult to assess objectively. Thus, comprehensive, practical, and low-cost treatments are still in demand. We have been advancing such treatments based on physicochemical principles. The objective of this research was to test the effect of the surficial application of a thin layer (¼"; 6.3 mm) of biochar on the mitigation of gaseous emissions (as the percent reduction, % R) from swine manure. Two types of biochar were tested: highly alkaline and porous (HAP) biochar made from corn stover and red oak (RO), both with different pH and morphology. Three 30-day trials were conducted with a layer of HAP and RO (2.0 & 1.65 kg∙m^-2, respectively) applied on manure surface, and emissions of ammonia (NH₃), hydrogen sulfide (H₂S), greenhouse gases (GHG), and odorous volatile organic compounds (VOCs) were measured. The manure and biochar type and properties had an impact on the mitigation effect and its duration. RO significantly reduced NH₃ (19-39%) and p-cresol (66-78%). H₂S was mitigated (16~23%), but not significantly for all trials. Significant (66~78%) reductions for p-cresol were observed for all trials. The phenolic VOCs had relatively high % R in most trials but not significantly for all trials. HAP reduced NH₃ (4~21%) and H₂S (2~22%), but not significantly for all trials. Significant % R for p-cresol (91~97%) and skatole (74~95%) were observed for all trials. The % R for phenol and indole ranged from (60~99%) & (29~94%) but was not significant for all trials. The impact on GHGs, isobutyric acid, and the odor was mixed with some mitigation and generation effects. However, larger-scale experiments are needed to understand how biochar properties and the dose and frequency of application can be optimized to mitigate odor and gaseous emissions from swine manure. The lessons learned can also be applicable to surficial biochar treatment of gaseous emissions from other waste and area sources.

Background (1) In the time of the 4th Industrial Revolution or Industry 4.0, a conglomerate of technical and social inventions, political contexts, socio-cultural circumstances, environmental policies, business models, and economic policies has emerged. Sustainability policy in theory and practice aims to deal with the effects of all these factors and to try to make decisions that ensure both social and economic development sustainably. The question is how to familiarize oneself with the current knowledge about the relationship between Industry 4.0 and sustainability?; (2) Methods: This research utilizes an automated content analysis method to analyses scientific journals, newspapers and magazines. The comparison of results of both research group shows that the scientific literature focuses more on changes in business models, production processes and technologies that enable sustainable development; (3) We found that the scientific literature focuses more on changes in business models, production processes and technologies that enable sustainable development. Newspapers and magazines articles write more about sustainable or green investment, sustainable standards and sustainable reporting. Newspapers and magazines articles write more about sustainable or green investment, sustainable standards and sustainable reporting. Newspapers, as well as some latest research journals, include articles of the COVID-19 outbreak and its effect on the economy and the environment. Indeed, the outbreak of the virus brings a new thought to the reorganization of the complex relationships between consumers, businesses and the state; (4) Conclusions: According to the comparison of the analyses of the results, it can is that the analyses of both types of literature, both scientific and professional, shows that there are common topics they write about, which are related to the field of clean production, emissions, renewable energy, climate change, sustainable investments and corporate sustainability. An urgent global issue that extends all over the world is the promotion of energy-saving technologies and reduction of carbon dioxide emissions.

Foot-and-mouth disease virus (FMDV) causes the highly contagious foot-and-mouth disease, which is characterized by the appearance of vesicles in and around the mouth and feet of cloven-hoofed animals. BHK21 cells are the cell line of choice for the propagation of FMDV for vaccine production world-wide but vary in their susceptibility for different FMDV strains. Previous studies showed that the FMDV resistance of a certain BHK cell line can be overcome by using a closely related but permissive cell line for the pre-adaptation of the virus, but the adapted strains were found to harbor several capsid mutations. In this study, these adaptive mutations were introduced into the original Asia-1 Shamir isolate individually or in combination to create a panel of 17 Asia-1 mutants by reverse genetics and examine the effects of the mutations on receptor usage, viral growth, immunogenicity and stability. A single amino acid exchange from glutamic acid to lysine at position 202 in VP1 turned out to be of major importance for productive infection of the suspension cell line BHK-2P. In consequence, two traditionally passage-derived strains and two recombinant viruses with a minimum set of mutations were tested in vivo. While the passaged-derived viruses showed a reduced particle stability, the genetically modified viruses were more stable but did not confer a protective immune response against the original virus isolate.

With the decline in fossil fuels, hydrogen-based alternatives provide a reliable and clean source for sustainable energy generation. In these endeavors, photochemical splitting for hydrogen production through tandem cells has been the source of much theoretical and experimental research in science. Much focus has been placed on interfacial band gap engineering as one of the most promising routes in the generation of hydrogen.This present work explores sputtering of n-silicon to form the active electrode in a n-Si | n-TiO₂ tandem cell and investigates the effect of variations in sputtering and post sputtering treatment parameters (rapid thermal annealing and long cycle annealing) for successful deposition of crystalline Silicon. The samples were successfully characterized via Raman Spectroscopy, X-ray Diffraction and Optical Transmission Spectroscopy to ascertain prevalent crystalline order and optical band gap, under different sputtering and post-sputtering conditions. Relevant conclusions were drawn to ascertain the best possible deposition parameters of n-Si for photocatalytic water splitting.

Abstract: Mass spectrometry research laboratories reported multiple probes for ambient ionization in the last years. Combining them with a mechanical moving stage enables automated sampling and imaging applications. We developed a robotic platform, which is based on RepRap 3D-printer components, and therefore easy to reproduce and to adopt for custom prototypes. The minimal step width of the Open LabBot is 12.5 μm, and the sampling dimensions (x, y, z) are 18 × 15 × 20 cm. Adjustable rails in an aluminium frame construction facilitate the mounting of additional parts such as sensors, probes, or optical components. The Open LabBot uses industry-standard G-code for its control. The simple syntax facilitates the programming of the movement. We developed two programs: 1) LABI-Imaging, for direct control via a USB connection and the synchronization with MS data acquisition. 2) RmsiGUI, which integrates all steps of mass spectrometry imaging: The creation of G-code for robot control, the assembly of imzML files from raw data and the analysis of imzML files. We proved the functionality of the system by the automated sampling and classification of essential oils with a PlasmaChip probe. Further, we performed an ambient ionization mass spectrometry imaging (AIMSI) experiment of a lime slice with laser desorption low-temperature plasma (LD-LTP) ionization, demonstrating the integration of the complete workflow in RmsiGUI. The design of the Open LabBot and the software are released under open licenses to promote their use and adoption in the instrument developers’ community.

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines Particle Swarm Optimisation (PSO) with a turbulence intensity based Jensen wake model (TI-JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ($C_P$) or deflecting wakes by applying yaw-offsets for maximising net farm production. First, TI-JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimized strategies are evaluated using simulations based on TI-JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 seconds for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.

Self-healing systems have become essential in modern computing for ensuring continuous and secure operations while minimising downtime and maintenance costs. These systems autonomously detect, diagnose, and correct anomalies, with effective self-healing relying on accurate interpretation of system logs generated by operating systems (OS). Manual analysis of these logs in complex environments is often cumbersome, time-consuming, and error-prone, highlighting the need for automated, reliable log analysis methods. Our research introduces an intelligent methodology for creating self-healing systems for multiple OS, focusing on log classification using CountVectorizer and the Multinomial Naive Bayes algorithm. This approach involves preprocessing OS logs to ensure quality, converting them into a numerical format with CountVectorizer, and then classifying them using the Naive Bayes algorithm. The system classifies multiple OS logs into distinct categories, identifying errors and warnings. We tested our model on logs from four major OS; Mac, Android, Linux, and Windows; sourced from Zenodo to simulate real-world scenarios. The model's accuracy, precision, and reliability were evaluated, demonstrating its potential for deployment in practical self-healing systems.

Using pumps operating as turbines (PATs) offers the possibility of increasing the sustainability of water and energy systems by recovering the excess of energy that would be otherwise lost in pressure reducing valves or head loss chambers. In on-grid applications, there are many research works and implementations of PATs. However, there is still limited research for the optimization of efficiency and stability of PATs operating in off-grid systems. This work contributes to the development of stable direct current (DC) off-grid electric systems based on PATs, by optimizing and analysing the energy efficiency and stability for a wide range of operation. A self-excited induction generator (SEIG) coupled to the PAT is provided to cover the conventional type of electric ma-chines typically coupled to the hydraulic pumps. In this context, a methodology is proposed, based on the hydraulic, mechanical and electric subsystems, to define the PAT-SEIG operational area, to maximize energy conversion and the system efficiency. This will guarantee stable electric output quantities when operating in a DC off-grid system. In addition, an analytical model is proposed to estimate the PAT-SEIG operation under specific conditions. With this, water managers are suitable to design and optimize an off-grid PAT-SEIG system and to define the best hydraulic machines, electronic equipment, and all different control elements to maximize energy conversion within the target of operational limits. To develop and verify this new methodology, two micro PAT-SEIG setups were implemented in the hydraulic laboratory of IST/CERIS under typical operating conditions. The proposed solution proved to be resilient under a variable load regime. The system's maximum efficiency can be adapted using different capacitor values for the excitation of the SEIG, and the operational range is limited to the electric components. Considering the nominal efficiencies of the system’s components, the maximum p.u. efficiency obtained for each PAT-SEIG system were between 0.7 and 0.8 p.u.

Transformer models have emerged as frontrunners in the field of natural language processing, primarily due to their adept use of self-attention mechanisms to grasp the semantic linkages between words in sequences. Despite their strengths, these models often face challenges in single-task learning scenarios, particularly when it comes to delivering top-notch performance and crafting strong latent feature representations. This challenge is more pronounced in the context of smaller datasets and is particularly acute for under-resourced languages such as Arabic. In light of these challenges, this study introduces a novel methodology for text classification of Arabic texts. This method harnesses the newly developed Reverse Positional Encoding (RPE) technique. It adopts inductive-transfer learning (ITL) framework combined with a switching self-attention shared encoder, thereby increasing the model's adaptability and improving its sentence representation accuracy. The integration of Mixture of Experts (MoE) and Reverse Positional Encoding (RPE) techniques empowers the model to process longer sequences more effectively. This enhancement is notably beneficial for Arabic text classification, adeptly supporting both the intricate five-point and the simpler ternary classification tasks. The empirical evidence points to its outstanding performance, achieving accuracy rates of 87.20% for the HARD dataset, 72.17% for the BRAD dataset, and 86.89% for the LABR dataset, as evidenced by the assessments conducted on these datasets.

The Reactive Spark Plasma Sintering (R-SPS) method is compared in this work with the two-steps SHS-SPS route, based on combination of the Self-propagating High-temperature Synthesis (SHS) with the SPS process, for the fabrication of dense (Hf0.2Mo0.2Ti0.2Ta0.2Nb0.2)B2-SiC and (Hf0.2Mo0.2Ti0.2Ta0.2Zr0.2)B2-SiC ceramics. A multiphasic and inhomogeneous product, containing various borides, is obtained at 2000°C/20min by R-SPS, from transition metals, B4C and Si. In contrast, if the same precursors are first reacted by SHS, and then processed by SPS under the optimized condition of 1800°C/20min, the desired ceramics are successfully attained. Both High-Entropy-Boride/SiC products display densities > 97%, uniform microstructures and contained only small amounts of residual oxides (<2.4 wt.%). The presence of SiC makes the sintering temperature milder, i.e. 150°C below to that needed by the corresponding additive-free system. Fracture toughness is also markedly improved, particularly when considering the Nb-containing system processed at 1800°C/20min, whereas KIC progressively decreases (from 7.35 to 5.36 MPa m1/2) as the SPS conditions become more severe. SiC addition is found to inhibit the volatilization of metal oxides like MoO3 formed during oxidation experiments, thus avoiding mass loss in the ceramics. The benefits above also likely take advantage of the fact that the two composite constituents are synthesized in parallel, according to the SHS-SPS approach, rather than being produced separately and combined subsequently, so that strong interfaces between them are formed.

As distinct human societies, three unique intentional communities are investigated regarding their preferred consensus decision-making practices. It is Identified that each has adopted a different form of consensus decision-making to solve potential group-wide interpersonal conflict. The individual attributes of these three consensus decision-making practices are considered, both from the perspective of maintaining group stability and in relation to individual member's mental health. The communities are a Canadian self-directed public senior elementary and secondary school, an annual English conference for those self-identifying as on the autistic spectrum, and a self-producing Korean popular music (K-pop) group. It is found that the intentional community and participants’ mental health are sustained regarding each of the three consensus decision-making practices. Nevertheless, the resulting decisions generate various stresses within the communities, both as a whole and concerning the individual members. To retain group cohesion and maintain individual mental health, these stresses must be recognized and understood by participants. The novel finding of this research is that, dependent on the time available for decision-making, and the members’ perspective adopted, intentional communities might practice more than one form of consensus decision-making and still support both group cohesion and individual mental health, maintaining the democracy of these distinct societies.