The need of research is often a complex issue. The standardization of methodologies, deeply rooted in the scientific method, has allowed humanity to progress. Since the fashion industry represents one of the most problematic industries globally, it is paramount to investigate the handicaps of fashion research practice. The goal of this systematic review is to outline the most used methodologies in the fashion research field that are being explored to achieve sustainability goals. The findings highlight the fragmentation of the literature in fashion research methodologies in relation to sustainability. Interviews and case studies are the most used methods while other less conventional, like Social Life Cycle Assessment, have been also tried. Finally, it is tested how ChatGPT can discuss and inform future routes to incorporate the findings. Together, this work reveals the handicaps of the fashion research methodologies and show an example on how ChatGPT can be assessed to help the fashion practitioners define research plans to achieve sustainable goals.

Multiple indicators have been used to assess the degree of exposure of fish to anthropogenic chemicals in their stream habitats. We hypothesized that brook stickleback (Culaea inconstans) in a headwater stream receiving urban and agricultural runoff (South Fork Whitewater River, SFWR) would exhibit poorer condition, reduced reproductive fitness, and greater left side-to-right side morphological asymmetry (i.e., fluctuating asymmetry or FA) than would fish from a nearby headwater stream with a forested drainage basin (Garvin Brook). Male and female fish were collected from both streams just prior to spawning in 2013-2015. In 2013 and 2014, fish were assessed for overall condition (Fulton’s K) and internal measures of condition (hepatosomatic index, HSI) and reproductive fitness (gonadosomatic index [GSI], total egg count, egg weight). In 2015, measurements of head length, jaw length, eye diameter, pectoral fin length, and pelvic fin length were made on both sides of each fish for use in assessing degree of FA. We observed declining condition with fish size, increased liver size, and reduced egg counts and egg size in female brook stickleback in SFWR relative to those from Garvin Brook. SFWR females had significantly higher FA than Garvin females for all structures assessed except pelvic fin length. FA also was slightly higher for all structures in SFWR males compared to Garvin males, but differences were not significant. A composite FA index combining all measurements from an individual fish into a single value displayed highly significant differences for female fish (SFWR FA >> Garvin FA), but not for male fish (SFWR FA = Garvin FA). Exposure of brook stickleback to reduced water quality in SFWR during early development appears to increase morphological asymmetry in female (but not male) fish, and continuing exposure to compromised water quality throughout life impacts both general condition and reproductive fitness of stickleback, especially older female fish, in SFWR.

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

Any Hospital’s primary goal is to restore human health and save lives through health services provided to patients, but at the same time, hazardous wastes are produced. Inconsistent management of unsafe wastes might cause adverse effects and other issues to workers, the environment and public health. Segregation is considered the critical stage for a successful Healthcare Waste Management. Mixing hazardous waste with non-hazardous waste will be avoided by correctly applying practices at the segregation stage. This study aimed to assess personnel’s knowledge about Infectious Medical Waste and segregation practices used at six wards in Nicosia General Hospital. An analytical cross-sectional study was conducted, and data were collected through a structured self-administered questionnaire. The Statistical Package of Social Science (SPPS) version 25 was used with a minimum statistical significance α=0.05. The study population was nurses, nurse assistants, ward assistants and cleaners working at the study wards. Out of 191 questionnaires, 82 were received, with a response rate of 42.93%. Most participants were female (72%) and nurses (85,4%). Participants had medium to good knowledge about Infectious Medical Waste management and good knowledge regarding segregation practices applied in their ward. Segregation was not carried out as it should have since most participants stated that Infectious Medical Wastes were mixed with non-hazardous waste. Correct answers given by the participants regarding color-coding of different Healthcare Waste categories was 67.5%, and only four answered correctly to all questions. Although participants knew segregation practices and the colour-coding process applied for Healthcare Waste, they didn’t use them satisfactorily. They applied methods regarding segregation without specific training, knowledge and guidance. Due to the issue's importance, training programs must be implemented and performed.

TiNbZrTa alloys are promising for multidisciplinary applications, such as refractory and biomedical purposes due to their high thermal stability and non-toxicity. Hardness and elastic modulus are among the key features for their adequate industrial applications. The influence of porosity and Ti/Ta ratio were investigated on TiNbZrTa alloys produced by three different processing routes, i.e., (i) blend element and posterior press and sintering (BE + P&S), (ii) mechanical alloying with press and sintering (MA + P&S), and (iii) arc melting and casting. Atomic diffusion was improved during processing, resulting in lower porosity in the following order: casting < MA + P&S < BE + P&S. Ta offered limited atomic diffusion due to its high melting point compared to other constituent elements. Thus, the total porosity of alloys increased with increasing the Ta contents, i.e., by lowering the Ti/Ta ratio. However, the Ti/Ta ratio did not considerably affect the bonding energy or the elastic modulus. Hardness was increased significantly in dense alloys compared to the porous ones. However, porosity and Ti/Ta ratio did not show a clear trend in hardness among the porous alloys.

Due to growing concerns about environmental pollution from plastic waste, plastic recycling research is gaining momentum. Traditional methods, such as incorporating inorganic particles, increasing cross-linking density with peroxides, and blending with silicone monomers, often improve mechanical properties but reduce flexibility for specific performance requirements. This study focuses on synthesizing silica nanoparticles with vinyl functional groups and evaluating their mechanical performance when used in recycled plastics. Silica precursors, namely sodium silicate, and vinyltrimethoxysilane (VTMS), combined with a surfactant, were employed to create pores, increasing silica's surface area. Early-stage introduction of vinyl functional groups prevented the typical post-synthesis reduction in surface area. Porous silica was produced in varying quantities of VTMS, and the synthesized porous silica nanomaterials were incorporated into recycled Polyethylene to induce cross-linking. Despite a decrease in surface area with increasing VTMS content, a significant surface area of 883 m2/g was achieved. In conclusion, porous silica with the right amount of vinyl content exhibited improved mechanical performance, including increased tensile strength, compared to conventional porous silica. This study shows that synthesized porous silica with integrated vinyl functional groups effectively enhances the performance of recycled plastics.

This study investigated the efficacy of carvone, abscisic acid (ABA), gibberellin (GA3), and variable temperature in managing dormancy and sprouting in aeroponically grown mini-tuber potato seeds. Results showed that carvone treatment effectively reduced weight loss rate by 12.25% and decay rate by 3.33% at day 25, compared to control. ABA treatment significantly enhanced germination rate to 97.33%. GA3 treatment resulted in the longest sprouts of 14.24 mm and reduced MDA content by 23.08% at day 30, indicating its potential in shortening dormancy and maintaining membrane integrity. Variable temperature treatment showcased a balanced performance in reducing weight loss and maintaining a lower relative conductivity, indicating less cellular damage. Enzymatic activities of α-amylase, CAT, and SOD were modulated by the treatments, ensuring a balanced enzymatic environment for seed vitality. These findings provide a robust foundation for refining post-harvest management strategies to optimize germination uniformity and preserve the quality of aeroponic potato seeds during extended dormancy, promising enhanced yield and productivity in potato cultivation.

The increasing demand for water resources in urban areas, such as Bangladesh, due to population growth is a significant concern. One potential solution under consideration is the use of air conditioning (AC) condensate water. A study conducted at the European University of Bangladesh (EUB) focused on assessing the quality and quantity of AC condensate water from various systems. The results indicate that the collected water generally adhered to the quality standards established for drinking and household use in Bangladesh. Parameters such as pH (averaging 6.8), turbidity (1.08 NTU), total dissolved solids (TDS) (averaging 219 mg/L), iron content (0 mg/L), alkalinity (averaging 41.67 mg/L), arsenic (0 mg/L), chemical oxygen demand (COD) (averaging 3.67 mg/L), biochemical oxygen demand (BOD) (averaging 1.33 mg/L), chloride content (averaging 30.77 mg/L), and other factors were evaluated. Moreover, the AC units at EUB, varying in cooling capacity (1 ton, 2 tons, and 4 tons), produced substantial monthly volumes of 96, 177, and 354 liters of condensate water, respectively. This underscores the potential of AC condensate water as a valuable resource for addressing urban water scarcity. As a result, there is a pressing need for local decision-makers and policymakers to establish well-defined guidelines for the effective utilization of AC condensate water to mitigate water scarcity issues in urban areas.

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly termed nonalcoholic fatty liver disease (NAFLD), is a widespread global health concern that affects around 25% of the global population. Its influence is expanding, and it is anticipated to overtake alcohol as the leading cause of liver failure and liver-related death worldwide. Unfortunately, there are no approved therapies for MASLD; as such, national and international regulatory health agencies undertook strategies and action plans designed to expedite the development of drugs for treatment of MASLD. A sedentary lifestyle and an unhealthy diet intake are important risk factors. Western countries have a greater estimated prevalence of MASLD partly due to lifestyle habits. Mitochondrial dysfunction is strongly linked to the development of MASLD. Further, it has been speculated that mitophagy, a type of mitochondrial quality control, may be impaired in MASLD. Thyroid hormone (TH) coordinates signals from the nuclear and mitochondrial genomes to control mitochondrial biogenesis and function in hepatocytes. Mitochondria are known TH targets and preclinical and clinical studies suggest that TH, thyroid receptor β (TR-β) analogs, and synthetic analogs specific to liver could be of therapeutic benefit in treating MASLD. In this review, we highlight how mitochondrial dysfunction contributes to development of MASLD, and how understanding the role of TH in improving mitochondrial function paved the way for innovative drug development programs of TH-based therapies targeting MASLD.

This study applies several natural language processing techniques for characterizing social groups by analyzing published text in social media. The resulting computed values are proposed as features for modeling social groups and measure distances or similarities among them, along further data that could help understanding group behavior according to language usage. In this study, we have analyzed the messages published on Twitter regarding patriotism by the main political parties in Spain. The results show that lexical resources and topic modelling algorithms are useful to model different groups and serve as comparison tools to better understand the topics these groups are talking about.

We propose that the short−term memory (STM), when processing a sentence, uses two uncorrelated processing units in series. The clues for conjecturing this model emerge from studying many novels of the Italian and English Literatures. This simple model, referring to the surface of language, seems to describe mathematically the input-output characteristics of a complex mental process involved in a reading/writing a sentence. We show that there are no significant mathematical/statistical differences between the two literary corpora by considering deep-language variables and linguistic communication channels, therefore, the surface mathematical structure of alphabetical languages is very deeply rooted in human mind, independently of the language used. The first processing unit is linked to the number of words between two contiguous interpunctions, variable Ip, approximately ranging in Miller’s 7±2 range; the second unit is linked to the number of Ip’s contained in a sentence, variable MF, ranging approximately from 1 to 6. The overall capacity required to process fully of a sentence ranges from 8.3 to 61.23 words, values that can be converted into time by assuming a reading speed, giving the range 2.64~19.54 seconds for fast-reading and 5.3~30.1 seconds for average reader. Since a sentence conveys meaning, the surface features we have found might be the starting point to arrive at an Information Theory that includes meaning.

Transport infrastructure’s asphalt pavement deteriorates under the influence of destructive factors. Damages which have been occurred during its exploitation period are repaired, and when their further rehabilitation is economically and technically irrational, the asphalt pavement is recycled. The material from the asphalt pavement layer that has reached its limit state is milled out or broken and crushed and then is repeatedly used in the production of hot-mix asphalt (HMA) or warm-mix asphalt (WMA) mixtures. In this paper, the dynamics of the percentage recycling ratio (RR) of old asphalt pavement material was investigated. RR represents the quantity of reclaimed asphalt pavement (RAP) used in the production of HMA and WMA mixtures in Europe and the USA, divided by the total amount of RAP prepared in the country. Factors and goals affecting it are analyzed. An original system of 10 criteria that increase the RR country has been created. By applying different multiple criteria decision making (MCDM) methods and using the importance given to these criteria by 14 experts, the normalized subjective weights of the criteria were determined. Analytic Hierarchy Process (AHP), rank correlation, Average Rank Transformation into Weight - Linear (ARTIW-L) and Non-linear (ARTIW-N), Direct Percentage Weight (DPW) methods were used in the study. The results display that the RAP recycle rate is close to 100% in countries with a sustainable economic background. In the Baltic countries, it is mostly increased by the adequacy of regulatory documents, the strategy promoting asphalt recycling in the country, and the homogeneity and classifying of RAP. The number and capacity of RAP stocks, the number and productivity of asphalt milling equipment and the wear and tear of the asphalt pavement have the least influence on the increase of RR. The opinions of experts in assessing the significance of all criteria are consistent. The averages of the weights of criteria determined by four MCDM methods (AHP, ARTIW-L, ARTIW-N, DPW) made it possible to obtain more reliable results. These results can be used to make strategic decisions and to create a plan of practical actions to increase the RAP recycling rate in the developing countries.

Keywords: Post-Stroke; Hyperbaric oxygen therapy; Sequelae; Feasibility; Out-patient.

This article shows how to establish expansion formulas for calculating the mixed operations $(A^{\dag})^{\#}$, $(A^{\#})^{\dag}$, $((A^{\dag})^{\#})^{\dag}$, $((A^{\#})^{\dag})^{\#}$, $\ldots$ of generalized inverses, where $(\cdot)^{\dag}$ denotes the Moore--Penrose inverse of a matrix and $(\cdot)^{\#}$ denotes the group inverse of a square matrix. As applications of the formulas obtained, the author constructs and classifies some groups of matrix equalities involving the above mixed operations, and derives necessary and sufficient conditions for them to hold.

This article addresses the need to know the thermal conductivity in different filaments that this FDM technology uses. Because there are multiple applications that these additive manufacturing products play in the field of thermal insulation. For the study of thermal conductivity, the DTC-25 commercial conductivity measurement bench is used, where the tests will be carried out on a set of seven different materials with 100% fabrication density. From base materials such as acrylonitrile butadiene styrene (ABS) or polylactic acid (PLA), materials of high mechanical and thermal resistance, polyether ether ketone (PEEK) and high-performance polyetherimide thermoplastic (ULTEM), to materials with metal inclusions (aluminum 6061) that would later be subjected to thermal aftertreatments. The study shows how the parts manufactured with aluminum inclusion have a higher thermal conductivity, 0.40 ±0.05 W/m·K, compared to another material, such as TPU, of high mechanical and thermal resistance, with a conductivity of 0.26 ±0.05 W/m·K.

The discovery of solitonlike coherent structure (SCS) in boundary layer flows is crucial for understanding turbulence origins and in particular for laminar-turbulence transition. However, the task of finding solutions for solitonlike coherent structure from the Navier-Stokes equations poses a significant challenge. In this paper, based on the author's previous work [B.H. Sun, Exact similarity solutions of unsteady laminar boundary layer flows, Preprints 2023, 2023092117], we are able to study convergent flow boundary layers, whose solution encompasses both shock wave and solitary wave solutions, and their superposition gives rise to solitary-like waves, namely solitonlike coherent structure. It is found that the solitonlike coherent structure can only be obtained by combining the Navier-Stokes equations and mass conservation, since the combined equations will have the third order derivatives respect to coordinate $x$, in other words, without the mass conservation condition, the Navier-Stokes equations does not contain solitonlike coherent structure by itself. Finally proved solitonlike coherent structure is a universal motion form of viscous fluid.

CO2 flooding stands as a pivotal technique for significantly enhancing oil recovery in low-permeability reservoirs. The movement and sweeping rules at the front of CO2 flooding play a critical role in its oil recovery, yet a comprehensive quantitative analysis remains an area in need of refinement. In this study, we have developed 1D and 2D numerical simulation models to explore the sweeping behavior of miscible, immiscible, and partly-miscible CO2 flooding patterns. The front position and movement rules of the three CO2 flooding patterns are determined. A novel approach of the contour area calculation method is introduced to quantitatively characterize the sweep coefficients, and the sweeping rules are discussed regarding geological parameters, oil viscosity, and injection-production parameters. Furthermore, the Random Forest (RF) algorithm is employed to identify the controlling factor of the sweep coefficient, as determined by out-of-bag (OOB) data displacement analysis. The results show that the miscible front is located at the point of maximum CO2 content in the oil phase. The immiscible front occurs at the point of maximum interfacial tension near the production well. Remarkably, the immiscible front moves at a faster rate compared to the miscible front. Geological parameters, including porosity, permeability, and reservoir thickness, significantly impact the gravity segregation effect, thereby influencing the CO2 sweep coefficient. Immiscible flooding exhibits the highest degree of gravity segregation, with a maximum gravity segregation degree (GSD) reaching 78.1. The permeability ratio is a crucial factor, with a lower limit of approximately 5.0 for reservoirs suitable for CO2 flooding. Injection-production parameters also play a pivotal role in sweep coefficient. Decreased well spacing and increased gas injection rates are found to enhance sweep coefficients by suppressing gravity segregation. Additionally, higher gas injection rates can improve the miscibility degree of partly-miscible flooding from 0.69 to 1.0. Oil viscosity proves to be a significant factor influencing the sweep coefficients, with high seepage resistance due to increasing oil viscosity dominating the miscible and partly-miscible flooding patterns. Conversely, gravity segregation primarily governs the sweep coefficient in immiscible flooding. In terms of controlling factors, the permeability ratio emerges as a paramount influence, with a factor importance value (FI) reaching 1.04. these results provide a theoretical foundation for the application of CO2 flooding, enhancing the understanding of the critical factors governing its success.

Sustained-release drug delivery systems have evolved significantly over the years, playing a pivotal role in pharmaceutical research and enhancing patient care [1]. This abstract synthesizes key insights from various references to highlight the noteworthy advancements in this field. Recent research has emphasized the importance of understanding drug properties and their impact on sustained-release system design [2] [9]. These advancements enable tailoring drug delivery to optimize therapeutic outcomes [2] [9]. Incorporating innovative technologies such as nanotechnology [1], biodegradable polymers [3], and matrix systems [4] has led to the development of more efficient and patient-friendly sustained-release systems [1] [3] [4] Moreover, the concept of personalized medicine is gaining prominence [5], opening doors to individualized drug delivery strategies [5]. These strategies promise to enhance treatment efficacy while minimizing adverse effects [5]. Theoretical analyses, as exemplified by Higuchi’s work [6], continue to shape the understanding of sustained-release mechanisms [6]. Furthermore, studies on enteric-coated timed-release tablets [10]. highlight advancements in specific drug formulations to meet patient needs [10]. In conclusion, advancements in sustained-release drug delivery systems reflect a dynamic landscape where drug properties, innovative technologies, and personalized approaches converge to improve patient outcomes and drug therapy [7] [8] [10].

Flood records of the Kunmalik River in Chinese Tien Shan mountains have showed that glacier- dammed lake outburst floods (GLOF) had occurred almost every year since 1956 from the glaci-er-dammed Lake Merzbacher. However, GLOF in cold season is seldomly studied. The defense and management of the flood need further observation and study. The maximum peak discharge of 825 m3/s occurred on 5th December 1996 with the maximum flood volume of 2.875×108 m3, which was 18 times larger than that of the mean daily discharge in winter over the past six decades. The drainage of the Merzbacher lake is not a mechanical breaking up of the glacier dam, but the release of pressurized water along a subglacial tunnel triggered by quickly surge of the Northern Glacier, the subglacial tunnels are enlarged by both ice melting and frictional heat in the flooding water and heat under the glaciated dam and the lake in winter. There is no correlation between the GLOF size and air temperature of cold season. The floods in cold season have become bigger since 1996 than that before, and the flood duration has been extended from winter to spring. Once the cold GLOF starts, its discharge can be forecasted day by day to extrapolate the monitoring hydrograph as a linear curve.

A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or system. Recent advancements have extended the concept of digital twins to humans, incorporating complex biological data such as DNA (Predictiv Care, Inc.) and immune system profiles. These sophisticated models go beyond mere pictorial representations, offering a more holistic digital reflection. However, a significant gap remains. The current human digital twin models are not capable of learning one’s preferences. In this pilot study, we introduce Diginality, a chatbot powered by HyperCLOVA X, a Large Language Model (LLM) developed by NAVER, Inc. Diginality, learns one’s preferences with custom training from data collected by interview-style questions on the user’s topic of interest. Our findings demonstrate that Diginality successfully answers one’s preferences, thereby adding a new dimension to the concept of human digital twins. This work represents a pioneering step towards creating a more comprehensive and psychologically nuanced human digital twin.

In this paper we compare the effects of forecasting demand using individual (disaggregated) components versus first aggregating the components either fully or into several clusters. Demand streams are assumed to follow autoregressive moving average (ARMA) processes. Using individual demand streams will always lead to a superior forecast compared to any aggregates, however we show that if several aggregated clusters are formed in a structured manner then these subaggregated clusters will lead to a forecast with minimal increase in mean-squared forecast error. We show this result based on theoretical MSFE obtained directly from the models generating the clusters as well as estimated MSFE obtained directly from simulated demand observations. We suggest a pivot-algorithm, that we call Pivot Clustering, to create these clusters. We also provide theoretical results to investigate sub-aggregation, including for special cases such as, aggregating MA(1) streams and ARMA streams with similar or same parameters.

The link between tourism and the agricultural sector offers on the one hand authentic cultural experiences to tourists and on the other hand a competitive advantage to tourism businesses seeking to differentiate themselves. This research attempts to empirically investigate the organizational readiness, perceived benefits, and intention of hotels to include local agri-food products in their menus. The data collection was carried out through a questionnaire using a 5-point Likert scale and the readiness was evaluated in terms of its three dimensions (organizational culture, organizational climate, organizational capacity), as well as the perceived benefits in 112 hotels of the PE of Kavala and Thassos in Greece. Following a cluster analysis, 3 categories of hotels emerged, namely “Committed”, “Hesitant”, and “Indifferent”. The findings make an important contribution to the scientific community and more specifically the one dealing with both the primary sector and tourism issues. The research also has important managerial implications and can be used for policy-making by stakeholders.

Detailed evaluation is provided for the likelihood statistics intrinsic to interlocking Sequential Bayes analysis, which allows estimation of evidential support for dendrograms charting the macroevolution of taxa. It involves complexity functions, such as fractal evolution, to generate well-supported evolutionary trees. Required are data on trait changes from ancestral species to descendant species, which is facilitated by reduction of large genera to monothetic groups (one ancestral species each), most conveniently named as separate genera. Assigning each species one Shannon informational bit per new trait, then adding the traits as sequential Bayesian analysis (each posterior probability used as the prior for the next iteration of Bayes Formula), then interpretation with an odds chart, provides a posterior probability that the ancestor-descendant order is correct, that is, entirely follows evolutionary theory involving adaptation and rarity of total trait reversals. The key fact is that the most recently acquired traits of the ancestral species are selectively inviolate and passed on without change to each descendant species. The details of sequential Bayesian analysis were clarified by calculation of likelihood ratios and Bayes factors that compare optimal models with the next most likely model. Such analysis demonstrated that the optimum arrangement of ancestor and descendant species leads to high support values for fitting evolutionary theory, comparable to statistical support levels reported for molecular evolutionary trees. The fact that phylogenetic analysis does not offer an ancestor-descendant model in is found to be conducive of precision at the expense of accuracy, and leads to wrong ancestor-free models with likelihoods explaining data at high credible levels. The number of advanced traits in the outgroup as a Bayesian prior greatly enhances posterior probability. The method is simple, free of special computer analysis, and well-suited to standard taxonomic practice.

Agricultural drainage canals that connect upstream fish spawning areas to downstream rivers and lakes serve as crucial habitats for migrating fish. However, disconnections, such as drops and chutes, have been constructed in these canals due to agricultural modernization and flood control measures, hindering the movement of fish with limited swimming abilities. Portable fishways offer a promising solution to reconnect water bodies in agricultural canals, as they can be easily removed during high water discharges to avoid impeding the canals' drainage function. In addition to experimental assessments of fishway functionality, employing a hydrodynamic model to explore effective placement strategies for portable fishways is essential to maximize their effectiveness. This study presents a method to determine the best horizontal location of a portable fishway in an agricultural drainage canal using two-dimensional hydrodynamic simulations within the specified cases. The applicability of this method is demonstrated by addressing the positioning challenge of a portable fishway on a chute in an agricultural drainage canal in Japan. Results indicate that the proposed method allows for the selection of a suitable location, considering preferable hydraulic conditions both within the portable fishway and around its entrance.

Air pollution is a global issue, and the transportation sector is recognized as the third-largest contributor to anthropogenic greenhouse gas emissions. Vehicles emit a range of chemical compounds as a direct result of the combustion process. The nature and quantity of these emissions depend on the vehicle's characteristics, road, and local weather conditions. As a result, these emissions require special attention due to the adverse effects contributing to global warming and significantly impacting human health. In this regard, diagnosing and monitoring air quality is crucial for understanding the nature and quantity of emissions generated by various sources. However, in developing countries, the necessary inputs, and data for conducting such analyses are not always available. Therefore, the purpose of this study is to estimate emissions specifically generated from road operations. To achieve this, HDM-4 calculation tool is utilized to quantitatively estimate these emissions. This tool was applied in Baja California, Mexico, on the Centinela-La Rumorosa highway. The results obtained show that annually, 372.5 tons of pollutant emissions are generated, composed of HC, CO, CO2, NOx, Par, SO2, and PB, covering a mere 128 kilometers of length within a state road network spanning 11,429 kilometers. This highlights the necessity of implementing strategies to reduce emissions or the environmental impact generated by vehicular operations on roads in developing countries.

Photon-counting Computed Tomography (PCCT) is a promising cardiovascular-imaging technique. Using detectors that count the number and energy of photons in multiple bins, PCCT offers several advantages over conventional CT, including a higher image quality, reduced contrast agent volume, radiation doses, and artifacts. Although PCCT is well established for cardiac imaging in assessing coronary artery disease, its application in vascular imaging remains limited. This review summarizes the available literature and provides an overview of the current use of PCCT for the diagnosis of vascular conditions. Of the 13 records selected in the initial search, 7 articles were included in the present review: 6 cohort studies and 1 case report. This review synthesized the available literature on PCCT in vascular imaging, focusing mainly on endoleaks detection and characterization after endovascular aneurysm repair (EVAR), contrast dose volume, and radiation exposure reduction, particularly in patients with chronic kidney disease and in those requiring follow-up CT.

Recently, nanobodies have taken center stage in research on drug discovery and development. Several therapeutic possibilities using nanobodies are presently undergoing clinical trials and waiting for FDA approval. The goal of this study was to emphasize the potential of nanobodies as therapeutic agents by concentrating on the most recent published studies that examined their properties, manufacturing, and possible applications. This article demonstrates that the unique properties of nanobodies in comparison to conventional antibodies, which are based on their small size and offer a number of benefits, make them seem to have a promising future. These advantages include the ability to access complex or hidden target sites that may be elusive to their larger antibody counterparts, and increased resilience against extreme conditions like tempera-ture changes and pH variations. As a result, nanobodies seem to have a bright future as adaptable tools for imaging in cancer and non-malignant diseases, as well as for in vitro and in vivo diagnostic and therapeutic potential for a variety of conditions, including oncology, infectious, metabolic, neurological, and other conditions like ophthalmologic, immune-mediated, and genetic disorders. More research is required to determine their effectiveness and safety in clinical applications. The current analysis offers a thorough overview of the therapeutic uses for nanobody products that are either on the market or undergoing clinical trials.

The classical joint economic lot-sizing (JELS) policy in a single-vendor single-buyer system generates an equal production quantity in all cycles, where the input parameters remain static indefinitely. In this paper, a new two-echelon supply chain inventory model is developed involving a hybrid production system that simultaneously focuses on green and regular production methods with optimal allocation fraction of green and regular productions. Unlike the classical mathematical formulation, each cycle is independent from the previous one, and consequently, the input parameters can be adjusted to be responsive to the dynamic nature of demand rate and price fluctuation. A rigorous heuristic approach is used to derive a global optimal solution for a joint hybrid production system. The model accounts for carbon emissions from production and storage activities related to green and regular produced items along with transportation activity under a multi-level emission-taxing scheme. The results emphasize the significant impact of green production on emissions. That is, the higher the allocation fraction of green production the lower the total amount of emissions generated by the system, i.e., the system becoming more sustainable. Adopting a hybrid production method not only decreases the greenhouse gas (GHG) emissions dramatically, but also reduces the per unit time total cost when compared with regular production. One of the main findings is that the total system cost generated by the base closed-form formula of the proposed model is considerably lower than that of the existing literature i.e., 33.59% (16.13%) lower in the first cycle (subsequent cycles) when the regular production method is assumed. Moreover, the optimal production rate generated by the proposed model is the one that minimizes the emissions production function. Illustrative examples and special cases that reflect different realistic situations are compared to outline managerial insights.

A very important role in determining the quality of water is the assessment of its microbiological quality. Water quality control, which could pose a direct threat to human health and life, is performed in the case of water produced at the water treatment plants, tap water or water in swimming pools. However, these traditional methods used to assess its quality are laborious and time consuming. In emergency and incidental situations, in the era of terrorist threats, the need for quick, reliable and reproducible microbiological determinations seems to be essential. In this study, an attempt was made to compare various methods of assessing the microbiological quality of water. The assessment was carried out for waters with different microbiological characteristics: surface waters, rainwater, groundwater, and water supply. The evaluation was carried out using traditional culture methods and high-speed methods: flow cytometry and luminometry. The analysis of microbiological parameters were the basis for the statistical analysis. The conducted microbiological analysis of various types of water, along with their statistical evaluation, showed different dependencies for each of the analysed waters.

Background: Hematopoietic cell transplantation (HCT) is an established therapy for hematologic malignancies and serious non-malignant blood disorders. Despite its curative potential, HCT is associated with substantial toxicity and health resource utilization. Effective delivery of HCT requires complex hospital-based care, which limits the number of HCT centers in Canada. In Canada, the quantity, indications, temporal trends, and outcomes of patients receiving HCT are not known. Methods: A retrospective cohort study of first transplants reported to the Cell Therapy Transplant Canada (CTTC) registry between 2000 and 2019. We determined overall survival (OS) and non-relapse mortality (NRM), categorizing the cohort into early (2000-2009) and later (2010-2019) eras to investigate temporal changes. Results: Of 18,046 transplants, 10,475 were allogeneic and 7,571 were autologous. Comparing the two eras, allogeneic transplants increased in number by 22.3%, with greater use of matched unrelated donors in the later era. Autologous transplants increased by 10.9%. Temporal improvements in NRM were observed in children and adults. OS improved in pediatric patients and in adults receiving autologous HCT. In adults receiving allogeneic HCT, OS was stable despite the substantially older age of patients in the later era. Interpretation: HCT is an increasingly frequent procedure in Canada which has expanded to serve older adults. Noted improvements in NRM and OS reflect progress in patient and donor selection, preparation for transplant, and post- transplant supportive care. In allogeneic HCT, unrelated donors have become the most frequent donor source, highlighting the importance of continued growth of volunteer donor registries. These results serve as a baseline measure for quality improvement and health services planning in Canada.

Contaminants of Emerging Concern (CECs), a new category of contaminants in limelight, is a major issue of global concern. The pervasive nature of CECs and its harmful effects, such as cancer, reproductive disorders, neurotoxicity etc., make the situation alarming. The perilous nature of CECs lies in the fact that even very small concentration of CECs can cause great impacts on living beings. It also has a nature of bioaccumulation. Thus, it is the ultimate need to have efficient sensors for detection of CECs to ensure safe living environment. Electrochemical sensors are an efficient platform for CEC detection as they are highly selective, sensitive, stable, reproducible, and prompt, and can detect very low concentrations of analyte. Major classes of CECs are pharmaceuticals, illicit drugs, personal care products, endocrine disruptors, newly registered pesticides, and disinfection by-products. This review focusses on CECs, including their sources and pathways, health effects caused by them, and electrochemical sensors as reported in literatures under each category for the detection of major CECs.

Tax compliance is a problem in Bangladesh, despite the tax system’s importance to economic growth. This research aims to identify the causes of tax compliance behav- ior issues and design customer-centric marketing techniques to increase compliance. The two-fold research strategy began with exploratory and descriptive analysis of primary survey data. The findings revealed a knowledge gap, particularly among younger populations, and showed that public acknowledgment and gamification can improve compliance. Next, experimental and causal research was used. Direct com- munication with 1,000 people and Phase 1 outcomes-based treatments were imple- mented. The results were promising: 95.74% had never experienced tax-related edu- cational programs, and 87% lacked tax calculation knowledge. When offered incen- tives like a 2% tax decrease, 73% were willing to pay taxes immediately. This study shows customer-focused marketing methods can solve Bangladesh’s tax compliance problem. Bridging the information gap and implementing motivating mechanisms can make our nation more obedient and financially stable.

This article provides a general overview of the bio-inspired computing method called DNA-Based Computing (DBC), including its theory and applications. The main theme of DBC is the central dogma of molecular biology (once information of DNA/RNA has got into a protein, it can’t get out again), i.e., DNA to RNA (sequences of four types of nucleotides) and DNA/RNA to protein (sequence of twenty types of amino acids) are allowed, not the reverse ones. Thus, DBC transfers few-element information (DNA/RAN-like) to many-element information (protein-like), solving a given cognitive problem. DBC can take many forms; this article elucidates two main forms, denoted as DBC-1 and DBC-2. Using arbitrary numerical examples, it is shown that DBC-1 can solve the following cognitive problems: “similarity indexing between seemingly different but inherently identical objects” and “recognizing regions of an image separated by a complex boundary.” DBC-2 can solve the following cognitive problem: “pattern recognition when the relevant information is insufficient.” Smart manufacturing-based systems (digital twins and big data analytics) must solve the abovementioned problems to make the manufacturing enablers (machine tools and monitoring systems) more self-reliant and autonomous. Consequently, DBC can improve the cognitive problem-solving ability of smart manufacturing-relevant systems and, thereby, can enhance its biologicalization.

Abstract: In recent years, the construction engineering sector has undergone a transformative shift towards the integration of machine learning (ML) techniques, particularly in predicting the properties of steel fiber-reinforced concrete (SFRC). Despite the theoretical sophistication of existing models, a persistent challenge remains – their opacity, lacking transparency and real-world relevance for practi-tioners. To address this gap and advance our current understanding, this study employs the extra gradient (XG) Boosting algorithm, crafting a comprehensive ap-proach. Grounded in a meticulously curated database drawn from 43 seminal pub-lications, encompassing 420 distinct records, this research focuses predominantly on three primary fiber types: crimped, hooked, and mil-cut. Complemented by hands-on experimentation involving 20 diverse SFRC mixtures, this empirical campaign is further illuminated through the strategic use of Partial Dependence Plots (PDPs), revealing intricate relationships between input parameters and con-sequent compressive strength. A pivotal revelation of this research lies in the identification of optimal SFRC formulations, offering tangible insights for real-world applications. The developed ML model stands out not only for its sophistication but also its tangible accuracy, evidenced by exemplary performance against inde-pendent datasets, boasting a commendable mean target-prediction ratio of 99%. To bridge the theory-practice gap, we introduce a user-friendly digital interface, thri-ughly designed to guide professionals in optimizing and accurately predicting the compressive strength of SFRC. This research thus contributes to the construction and civil engineering sectors by enhancing predictive capabilities and refining mix designs, fostering innovation, and addressing the evolving needs of the industry.

Cambodia faces the challenge of managing excess water during the wet season and insufficient water during the dry season. This harms human life and endangers aquatic and natural resources, agricultural practices, and food security. In order to ensure the well-being of both people and food security, water governance is crucial. However, Cambodia's water governance is hindered by various obstacles, including sectoral and centralized influences, top-down and large-scale strategies, a lack of coordination among relevant agencies, and limited involvement of local communities. This study delves into water governance across different sectors, from centralized to community-based natural resources management to tackle these challenges. Through analyzing literature and case studies of farmer water user communities (FWUC), community fisheries (CFis), and community fish refuges (CFRs) in three Mekong Delta provinces in Cambodia, the study concludes that although water governance has improved, it has resulted in a decline in fishery resources from rivers and water bodies and an increase in water conflicts among farmers and sectors in the face of climate change. To enhance water governance in Cambodia, it is critical to integrate it at the district level. This will promote sustainable water use and management across the country and pave the way for a brighter future.

Major depressive disorder (MDD) is a highly prevalent, debilitating disorder with a high rate of treatment resistance. One strategy to improve treatment outcomes is to identify patient-specific, pre-intervention factors that can predict treatment success. Neurophysiological measures such as electroencephalography (EEG), which measures the brain’s electrical activity from sensors on the scalp, offer one promising approach for predicting treatment response for psychiatric illnesses, including MDD. In this study, a secondary data analysis was conducted on the publicly available Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) database. Specifically, hierarchical regression modeling was used to predict treatment response from baseline demographics, symptom severity, and resting-state EEG features in 119 MDD patients receiving repetitive transcranial magnetic stimulation (rTMS). Across models, both age and baseline symptom severity, assessed by the Beck’s Depression Inventory, were significant predictors of rTMS treatment response, with older individuals and more severe depression scores associated with decreased odds of a positive treatment response. EEG measures contributed predictive power to these models; however, these improvements in outcome predictability only trended towards statistical significance (p~0.07 in multiple models). These findings provide confirmation of previous demographic and clinical predictors, while pointing to EEG metrics that may provide predictive information in future studies.

The present paper reports an exact approach quantifying the electromagnetic contribution to the anomalous magnetic moment occurring in isolated system comprised of non-composite particle carrying elementary electric charge. Essential averaging procedure and regularization of the electromagnetic field potentials necessary when quantifying the electromagnetic self-interactions and when deriving equations of motion without singularities and obeying the conservation laws are thoroughly discussed. The study shows that the dynamics of the considered system is associated to unique classical transcendental equations of motion satisfied by the particle's velocity and the electromagnetic contribution to the anomalous $g$-factor known from the quantum electrodynamics. The equations of motion lead to an exact analytical expression for the anomalous $g$-factor that provides more accurate result than that calculated with the aid of quantum electrodynamics. It matches the experimentally measured value reported in the literature to a one part per trillion. We obtain a_e=0.00115965218000(65) thus reveling the potential of non-perturbative, non-probability methods in predicting the electron's anomalous g-factor.

Biosynthetic gene clusters are specific genomic regions in microorganisms, like bacteria and fungi, responsible for producing bioactive compounds. Identifying these clusters is complex due to their diverse nature. This research presents a comprehensive approach to effective BGC identification. The study focuses on five classes of Natural Products: PKS , NRPS, RiPP, Terpenes, and Hybrid PKS-NRPS. Data was gathered from the MiBIG database in GBK format. Protein sequences from each file were extracted, and sequences under the same BGC ID were combined. Physicochemical properties were calculated, and sequence embeddings were generated using NLP techniques like CountVec, TFIDF, and Word2Vec specific to each NP class. An integrated feature matrix was created by merging physicochemical properties and generated embeddings. This matrix was used for training and testing of nine ML models such SVM, RF and many more. The study explored data balancing techniques with and without SMOTE and employed Grid Search for parameter optimization. This led to six datasets and 54 models. The LR model, using TFIDF with SMOTE, emerged as the most effective, achieving an accuracy of 0.96, AUC of 0.9912, and other strong metrics. This method enhances BGC identification for drug development, offering a broader understanding of their applications in medicine and biotechnology.

In the last years, new numerical methods have been applied to weather data for water budget es-timation. Geostatistics is one of the most powerful approaches, when it comes to studying spatially relevant natural phenomena, as it takes into account the spatial correlation among measurements over a specific study area and increases the reliability of the estimation by providing the associate uncertainty. In this study, we tested the feasibility of using a geostatistical method called Ordinary Kriging to provide a reliable estimation of the water budget of the Foro river basin (Periadriatic area, Central Italy), by comparing the obtained results with a more traditional yet robust approach based on the Thiessen polygon spatialization method, considered here as a reference. The spatial estimation with the selected geostatistical and traditional approaches was performed on the av-erage monthly rainfall and temperatures derived from 33-year-long time series measured at the weather station of the regional monitoring network. To transform the spatial variables quantities of both rainfall and air temperature into actual runoff estimates that ideally represent the river dis-charge, we used both the Turc and Thornthwaite methods, considering variable Potential Infiltra-tion Coefficients according to the hydrogeological complexes’ distribution. For the geostatistical method, the same procedure was applied to the 95% confidence interval limits, to quantitatively assess the possible impact of the spatial uncertainty on the estimation of the river discharge. The results obtained by both methods, subsequently converted into monthly discharges, were com-pared to the average monthly discharges related to 33-year-long time series measured at different locations along the Foro river. The results obtained with the geostatistical approach proved to be in line with the ones from the traditional method. Additionally, using the rainfall and temperature values obtained with the Ordinary Kriging and the corresponding confidence interval limits, it was possible to quantify the uncertainty associated with the discharge values, making the estimates more reliable than the ones obtained with the traditional approach. However, the yearly distribu-tion of river discharge obtained by both the geostatistical and traditional methods appeared to be dissimilar to the measured ones. In such anthropized and groundwater-dependent river basins, the surface water use as well as the regulatory effect of the alluvial aquifer regime may affect the river discharge variability over the year and then can account for similar discrepancies between the in-flow and outflow water volumes.

Inflammation involves the activation of innate immune cells and is believed to play an important role in the development and progression of both infectious and non-infectious diseases such as neurodegeneration, rheumatoid arthritis or cancer. Inflammation in the brain is marked by the upregulation of the translocator protein (TSPO) in microglia. Moreover, high TSPO levels are found, for example in macrophages during rheumatoid arthritis and in malignant tumor cells compared to its relatively low physiological expression. The same applies for cycloxygenase-2 (COX-2), which is constitutively expressed in the kidney, brain, thymus and gastrointestinal tract, but induced in microglia, macrophages and synoviocytes during inflammation. This puts TSPO and COX-2 in the spotlight as important targets for the diagnosis of inflammation. Imaging modalities, such as positron emission tomography and single-photon emission tomography, can be used to localize inflammatory processes and to track their progression over time. They could also enable to monitor the efficacy of therapy and predict its outcome. This review focuses on the current development of PET and SPECT tracers not only for the detection of neuroinflammation, but also for emerging diagnostic measures in infectious and other non-infectious diseases like rheumatic arthritis, cancer, cardiac inflammation and in lung diseases.

Preconception or prenatal carrier screening for hereditary deafness is an effective early intervention strategy to reduce the incidence of newborn deafness, but current research is limited. This study aimed to investigate the carrier frequencies of common deafness genes in the Chinese population and to follow up on pregnancy outcomes in high-risk couples. Carrier screening for common deafness genes in the Chinese population, including the GJB2 and SLC26A4 genes, was performed using next-generation sequencing technology. Of the 9,993 subjects screened, the carrier rate was 2.86% for the GJB2 gene and 2.63% for the SLC26A4 gene. Of the six high-risk couples, four made alternative reproductive decisions (three with prenatal diagnosis and one with preimplantation genetic testing), with consequent successful termination of the birth of two affected fetuses. The frequencies of preconception or prenatal carrier screening for the deafness genes GJB2 and SLC26A4 in 9,993 subjects from China in this study were 2.86% and 2.63%, respectively. In addition, four out of six high-risk couples made alternative reproductive decisions, followed by the successful prevention of the birth of two affected fetuses. These findings confirmed the clinical utility of preconception or prenatal carrier screening for hereditary deafness.

In this paper we report on the use of an in-house developed solid-state hybrid microwave and radiofrequency generator operating at frequencies of 2.45 GHz and/or 480 kHz for cancer ablation in various tissues. The generator has demonstrated increased accuracy due to its custom-designed hardware, which incorporates the latest technologies and algorithms to control the emitted power versus temperature profile of the treated sample. In particular, the hybrid generator incorporates control systems based on impedance or reflected power measurements that allow controlled ablation without causing unwanted carbonisation and without including areas where tissue damage is not desired. Tests performed ex-vivo on chicken breast and bovine liver show that radiofrequency ablation (RFA) is very effective for performing controlled ablations with minimally invasive probes, such as cardiac pathologies, small lesions, tissues with particular composition, while microwave ablation (MWA) is optimal for performing large ablations in highly vascularised tissues, such as liver cancer, where it is necessary to achieve higher temperatures.

Global Earth Observation (EO) is becoming increasingly important in understanding and addressing critical aspects of life on our planet about environmental issues, natural disasters, sustainable development and others. EO plays a key role in making informed decisions on applying or reforming land use, responding to disasters, shaping climate adaptation policies etc. EO is also becoming a useful tool for helping professionals make the most profitable decisions, e.g., in real estate or the investment sector. Finding similarities in landscapes may provide useful information regarding applying contiguous policies, taking alike decisions or learning from best practices on events and happenings that have already occurred in similar landscapes in the past. However, current applications of similar landscape retrieval are limited by moderate performance and the need for time-consuming and costly annotations. We propose splitting the similar landscape retrieval task into a set of smaller tasks that aim at identifying individual concepts inherent to satellite images. Our approach relies on several models trained with Unsupervised Representation Learning (URL) on Google Earth images to identify these concepts. We show the efficacy of matching individual concepts for tackling the task of retrieving similar landscape(s) to a user-selected satellite image with a proof-of-concept application of the proposed approach on the geographical territory of the Republic of Cyprus. Our results demonstrate the efficacy of breaking up the landscape similarity task into individual concepts closely related to remote sensing instead of trying to capture all concepts and image semantics with a single model like a single RGB semantics model.

Climate change and net zero are expected to have a significant impact on eco-nomic activity in developing countries, and thus, on the financial cash flow of organizations. In this paper, we propose a procedure for assessing the financial impact of climate change on organizational cash flow in developing countries. Our study contributes to the research on scenario-based climate change impact analysis. The applied research design is exploratory and mainly qualitative, constructing financial impact scenarios combined with narratives to determine the impact of climate change on individual cash flow line items. The narra-tives rationalize cash flow variations and enrich the estimates by adding expert knowledge, in the form of financial micro-stories and descriptions to the pro-posed procedure. We present a hypothetical citrus farming company in a de-veloping country to demonstrate the application of the proposed measurement procedure. The results obtained suggest that (1) the procedure is practical be-cause financial cash flows are known and widely used in financial analysis and because they can be implemented in a spreadsheet; that (2) the procedure pro-vides flexibility because it can be adapted to suit the specific needs of different companies, depending on a selected climate change scenario, the company’s exposure to climate change, its resilience and vulnerability; (3) that the prac-tice of thinking in terms of financial worst-case, base-case and best-case sce-narios, which reflect different levels of uncertainty, helps to model the finan-cial impacts of climate change and provides a forward-looking perspective on how much a company’s cash flow could change.

Bimetallic colloidal CoPt nanoalloys, with a very low platinum content, were successfully synthesized following a modified polyol approach. Powder X-ray diffraction, IR spectroscopy, thermogravimetric analysis, and Transmission Electron Microscopy studies were performed to estimate the crystal structure, morphology and surface functionalization of the colloids respectively, while the room temperature magnetic properties were measured using Vibrating Sample Magnetometer. The particles exhibit excellent uniformity, with a narrow size distribution, and display strong room temperature hysteretic ferromagnetic behavior even in the as-made form. Upon annealing at elevated temperatures, progressive formation and co-existence of exchange coupled both chemically ordered and disordered phases, significantly enhance the room temperature coercivity.

In Geo Polymer bricks (GPB), fly ash content which is a waste from power plants is converted into bricks by chemical treatment. GPB can be dried by using appropriate curing methods. Conven-tionally electric oven curing is one of the prominent methods. Using solar dryer instead of elec-trical oven gives added advantage of saving high grade electrical energy. So in this work solar dryer with PCM (Phase Change materials) Paraffin Wax and without PCM is used for curing ap-plication. GPB are gaining added advantage when compared to conventional bricks like cement and sand bricks in terms of strength. GPB has been taken as specimen for comparing the com-pressive, tensile split strength and flexural strength by electrical energy based curing and solar energy based curing. It is experimentally observed that solar energy based curing with PCM and without PCM exhibits higher compressive strength, higher tensile split strength and flexural strength when compared to electrical energy based curing. Solar curing with PCM shows higher compressive strength, higher tensile split strength and flexural strength when compared to Solar curing without PCM. Open solar curing is a traditional technique but nowadays aggressive cli-matic conditions can lead to severe damage of Geo polymers. The novelty of this work is to suggest to study the effect of PCM like paraffin wax in solar drying on curing time, mechanical properties of GPB.

Recreational and leisure areas play a special role. These areas mitigate or eliminate the disadvantages of living in large cities, and they enhance the quality of life in small towns. The aim of this study was to analyze the share of recreational and leisure areas in Polish counties in 2022. In Poland, recreational and leisure areas constitute a separate land-use category in cadastral registers. Annual cadastral data from the Land and Building Register at county level (in tabular form) were the main source of data for the study. The analysis revealed that the share of recreational and leisure areas was higher in counties situated in western and south-western Poland, as well as in cities. The spatially varied influence of socioeconomic factors on the share of recreational and leisure areas in Polish counties in 2002 was determined with the use of local Moran's I statistic and geographically weighted regression (GWR). The study confirmed that population density was significantly related to the share of recreational and leisure areas in Polish counties. The impact of the remaining socioeconomic factors associated with spatial and economic development varied across regions. The study also revealed that in addition to the current socioeconomic determinants, the share of recreational and leisure areas in Polish counties was also influenced by historical factors and the counties' development since their establishment.

mRNA vaccines have attracted widespread research attention with clear advantages in terms of molecular flexibility, rapid development, and potential for personalization. However, current mRNA vaccine platforms have not been optimized for induction of CD4/CD8 T cell responses. In addition, mucosal administration of mRNA based on lipid nanoparticle technology faces challenges in clinical translation. In contrast, adenovirus-based vaccines induce strong T-cell responses and have been approved for intranasal delivery. To leverage the inherent strengths of both the mRNA and adenovirus platforms, we developed a novel modular adenoviral mRNA delivery platform based on Tag/Catcher bioconjugation. Specifically, we engineered adenoviral vectors integrating Tag/Catcher proteins at specific locales on the Ad capsid proteins, allowing us to anchor mRNA to the surface of engineered Ad viruses. In proof-of-concept studies, the Ad-mRNA platform successfully mediated mRNA delivery and could be optimized via the highly flexible modular design of both the Ad-mRNA and protein bioconjugation systems.

We used a metal-organic chemical vapor deposition technique to synthesize a boron-doped reduced graphene oxide (B-rGO) material with various electrical characteristics, dependent on simultaneous reduction and doping procedures. The effect of the doping level on the B bonding in the reduced graphene oxide (rGO) layer controlled by the annealing temperature was investigated using X-ray photoelectron spectroscopy (XPS). The XPS data indicated that the B-rGO layer had a higher B concentration and a considerable number of O-B bonds as a result of the appreciable annealing temperature, which resulted in a decreased work function and Schottky contact between B-rGO and n-type Si. Owing to the higher proportion of B-C and B-C3 bonding in the B-rGO device than that in the rGO/Si device, the lower Schottky barrier height of the B-rGO/n-Si vertical junction photodetector resulted in a higher responsivity. This work suggests a facile method of B doping to alter the electrical properties of graphene materials.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

From the first isolation of the cystovirus bacteriophage Φ6 from Pseudomonas syringae 50 years ago, we have progressed to a better understanding of the structure and transformations of the many parts of the virion. The three-layered virion encapsulating the tripartite double stranded RNA (dsRNA) genome, breaches the cell envelope on infection, generates its own transcripts, and coopts the bacterial machinery to produce its proteins. The generation of new virions starts with a procapsid with a contracted shape, followed by packaging single stranded RNA segments with concurrent expansion of the capsid, and finally replication to reconstitute the dsRNA genome. The outer two layers are then added, and the fully formed virions released by cell lysis. Most of the procapsid structure composed of the proteins P1, P2, P4 and P7 is now known, as well as its transformations to the mature, packaged nucleocapsid. The outer two layers are less well studied. One additional study investigated the binding of the host protein YajQ to the infecting nucleocapsid, where it enhances the transcription of the large RNA segment that codes for the capsid proteins. Finally, we relate the structural aspects of bacteriophage Φ6 top those of other dsRNA viruses, noting the similarities and differences.

Background: Autoimmune inner ear disease (AIED) is a rare autoimmune condition characterized by progressive sensorineural hearing loss with hearing fluctuations over weeks to months, presenting significant challenges in both diagnosis and treatment. Due to its rarity, treatment responses and prognosis in AIED remain poorly understood. Methods: This study reviewed medical records from Taipei Medical University Hospital documented between August 2016 and August 2021. Among 67 patients experiencing fluctuating hearing loss, 11 met the diagnostic criteria for AIED and underwent regular follow-up assessments within a 5-year timeframe. ROC curve was used to discriminate the power of these prognostic parameters. Results: In this study, several factors including younger age, milder initial hearing loss, meeting criteria for bilateral Meniere's disease (MD), having an ascending audiogram pattern, longer time interval between hearing impairment attacks in contralateral ears and responding well to steroids were associated with better prognosis. ROC curve demonstrated the diagnostic value of specific parameters such as the presence of bilateral MD (AUC 0.825, 95% CI 0.640–1.000), pretreatment HI ≤ 45 dB HL (AUC 0.808, 95% CI 0.610–1.000), response to steroids (AUC 0.800, 95% CI 0.595–1.000), and ascending audiogram (AUC 0.858, 95% CI 0.683–0.858). Conclusions: This study identified key prognostic factors for AIED, providing valuable insights for predicting AIED prognosis and facilitating personalized patient management.

Since the photocatalytic reaction is a surface reaction, enhancing the gas movement around photocatalyst would promote photocatalytic CO₂ reduction performance. A new approach to enhance the gas movement around the photocatalyst by the natural thermosiphon movement of gasses around photocatalyst using black body material was proposed and confirmed experimentally, but the heat transfer mechanism on the phenomena was not clarified yet. The purpose of this study is to clarify the heat transfer mechanism of it. This study calculated the temperature of mixed gases of CO₂ and NH₃ around P₄O₁₀/TiO₂ photocatalyst by the heat transfer formula. It is revealed that there was no difference between the increase temperature (T_g) from the temperature at the beginning of the CO₂ reduction experiment (T_ini) and the temperature of mixed gases of CO₂ and NH₃ measured by thermocouple in the experiment (T_e) under the illumination condition with a visible light (VIS) + an infrared light (IR) and IR only. The heat transfer model proposed by this study has predicted T_g well under the illumination condition with VIS + IR and IR only well. On the other hand, it is revealed that the difference between T_g and T_e was as large as 10 ℃ under the illumination condition with an ultra violet light (UV) + VIS + IR.

Gastric perforations and leaks are significant complications that can arise from various gastrointestinal disorders and surgical interventions. Over the past decade, endoscopic techniques have emerged as effective method for managing these conditions. Furthermore, as endoscopic resection techniques have grown in popularity, the risk of acute and delayed perforations has also grown. This review aims to provide an in-depth analysis of the endoscopic management strategies employed in the management of gastric perforations and leaks. We will discuss the etiology, diagnostic modalities, and various endoscopic techniques utilized, and emerging trends in endoscopic management. Our attempt in writing this review paper is to educate and guide clinicians in making informed decisions when faced with gastric perforations and leaks.

Yogurt ice cream (YIC) containing Bifidobacterum longum subsp. longum TISTR 2195 (B. longum) with different fat contents (2 and 10% fat) and prebiotics (galacto-oligosaccharide, GOS and fructo-oligosaccharide, FOS) was investigated for its physicochemical properties, microstructure, and survival of B. longum during storage time and in a simulated gastrointestinal tract. Fat contents affected pH, fat particle size, hardness, melting rate, and solid-like characteristic of ice cream while FOS seemed to enhance the hardness and elasticity of YIC better than GOS due to less solubility and denser microstructure which exhibited more solid-like properties than YIC containing GOS. The interaction effect of fat content at 10% fat and FOS in YIC seemed to promote more sensorial acceptance than the ice cream containing 2% fat and GOS. The fat content, concentration of prebiotics, and type of prebiotics were attributed to the survival of B. longum in YIC. Moreover, the survival number of B. longum in YIC during storage for 180 days and in the simulated gastrointestinal tract was still in 106 CFU g-1, which is considered to benefit human health. Finally, probiotic YIC in this study could be used as a guide for functional ice cream manufacture future.

AML is a highly aggressive and heterogeneous form of hematological cancer. Proteomics-based stratification of patients into more refined subgroups may contribute to a more precise characterization of the patient-derived AML cells. Here, we reanalyzed liquid chromatography-tandem mass spectrometry (LC-MS/MS) generated proteomic and phosphoproteomic data from 26 chemoresistant/relapse (RELAPSE) and 15 relapse-free (REL_FREE) AML patients. We considered not only the RELAPSE and REL_FREE characteristics but also integrated the French-American-British (FAB) classification, along with considering the presence of nucleophosmin 1 (NPM1) mutation and cytogenetically normal AML. We found a significant number of differentially enriched proteins (911) and phosphoproteins (257) between the various FAB subtypes in RELAPSE patients. Patients with the myeloblastic M1/M2 subtype showed higher levels of RNA processing-related routes and lower levels of signaling related to terms like translation and degranulation, when compared to the M4/M5 subtype. Moreover, we found that high abundance of proteins associated to mitochondrial translation and oxidative phosphorylation, particularly observed in the RELAPSE M4/M5 NPM1 mutated subgroup, distinguishes relapsing from non-relapsing AML patient cells with the FAB subtype M4/M5. Thus, the discovery of subtype-specific biomarkers through proteomic profiling may complement the existing classification system for AML and potentially aid in selecting personalized treatment strategies for individual patients.

Background and Objectives: The severity of acute pancreatitis (AP) is graded as mild (MAP), moderately severe (MSAP), and severe (SAP) according to the revised Atlanta Classification. Determining the severity of AP is the main goal in the early time of AP. The aim of this study was to compare laboratory parameters or indices including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and neutrophil-creatinine index (NCI) at admission for predicting severity of AP. Materials and Methods: In this retrospective study, the data from patients who admitted with diagnosis of AP between January 2018 and December 2021 were collected. Laboratory parameters and indices including NLR, PLR and NCI were compared in the discrimination of AP severity groups. Results: A total of 421 patients [MAP group (n=213), MSAP group (n=158) and SAP group (n=50)] were included. WBC count, neutrophil count, hematocrit, CRP, BUN, creatinine, NLR and NCI were significant parameters to discriminate AP subgroups. The AUROC analysis for discriminating MSAP from MAP showed that WBC (AUC: 0.723, 95% CI: 0.674-0.768, p<0.001), hematocrit (AUC: 0.566, 95% CI: 0.514-0.607, p=0.032), NLR (AUC: 0.649, 95% CI: 0.598-0.698, p< 0.001) and NCI (AUC: 0.749, 95% CI: 0.701-0.792, p<0.001) were statistically significant parameters. Similarly, AUCs to discriminate SAP from MSAP were (AUC: 0.649, 95% CI: 0.579-0.713, p < 0.001) for WBC, (AUC: 0.703, 95% CI: 0.621-0.785, p< 0.001) for BUN, (AUC: 0.602, 95% CI: 0.532-0.669, p=0.029) for CRP and (AUC: 0.742, 95% CI: 0.676-0.800, p<0.001) for NCI. Logistic regression analyses indicated that NCI was the only parameter to discriminate MSAP form MAP (OR: 1.119, 95% CI: 1.015-1.235, p=0.023) and SAP from MSAP (OR: 1.113, 95% CI: 1.042-1.888, p = 0.001). Conclusions: The present study enabled the identification of neutrophil-creatinine index as a new prognostic tool for the assessment AP severity at hospital admission.

Post operative recurrence (POR) is the rule in patients with Crohn’s disease (CD), mitigated with prophylactic therapy. The evidence for therapeutic choice and timing of intervention is lacking. We aimed to compare rates of POR in patients treated early with prophylactic 6-mercaptopurine (6-MP) or adalimumab. We conducted a prospective single center randomized open label clinical study, in which patients in surgical remission following their first ileocecectomy were randomized to receive early treatment with 6-MP or adalimumab. Patients were followed up clinically every 3 months and underwent endoscopy at weeks 32 and 58 post-operatively. The primary endpoint was endoscopic recurrence (ePOR) at 1 year (week 58), defined as a Rutgeerts score≥i2. We enrolled 35 patients (25 males, mean age 35±1.4 years, median disease duration 5±6.1 years) following ileocecectomy. Of these, 7(20%) were current smokers and 9(26%) biologics experienced. Patients allocated to adalimumab had significantly less ePOR than patients treated with 6MP at week 32 (21% vs 69%, p=0.004) and 58 (47% vs 75%), (p=0.03, HR=0.39, 95% CI=0.16-0.93). POR was associated with increased diameter of the resected small bowel surgical specimen, lower baseline body mass index (BMI), increased week 18 fecal calprotectin and serum alanine aminotransferase and decreased week 18 hemoglobin level. Adalimumab is more effective than 6-MP in preventing ePOR. Increased operative small bowel diameter and lower post-operative BMI were associated with ePOR. Eighteen weeks serum hemoglobin and ALT, and fecal calprotectin levels were predictive of endoscopic disease recurrence. (ClinicalTrials.gov ID NCT01629628).

Developing a biomolecular detection method that minimizes photodamage while maintaining an environment suitable for biological constituents to maintain their physiological state is expected to drive new diagnostic and mechanistic breakthroughs. In addition, ultra-sensitive diagnostic platforms are needed for rapid and point-of-care technologies for various diseases. Considering this, surface-enhanced Raman scattering (SERS) is proposed as a non-destructive and sensitive approach to address the limitations of fluorescence, electrochemical, and other optical detection techniques. However, to advance the applications of SERS, novel approaches to enhance the signal of substrate materials are needed to improve reproducibility and costs associated with manu-facture and scale-up. Due to their physical properties and synthesis, semiconductor-based nanostructures have gained increasing recognition as SERS substrates; however, low signal en-hancements have offset their widespread adoption. To address this limitation and assess the po-tential for use in biological applications, zinc oxide (ZnO) was coated with different concentra-tions (0.01-0.1 M) of gold (Au) precursor. When crystal violet (CV) was used as a model target with the synthesized substrates, the highest enhancement was obtained with ZnO coated with 0.05 M Au precursor. This substrate was subsequently applied to differentiate exosomes derived from three cell types to provide insight into their molecular diversity. We anticipate this work will serve as a platform for colloidal hybrid SERS substrates in future bio-sensing applications.

In this article some important observations have been reported on recent works related to weakly tripotent rings and locally invo-regular rings. Our findings give additional results as well as correct some recent results on weakly tripotent rings and locally invo-regular rings appeared in Rendiconti Sem. Mat. Univ. Pol. Torino (2021) and Azerbaijan Journal of Mathematics (2021) respectively. In addition we exhibit that if the Jacobson radical J(A) of a ring is strongly involution t-clean then the characteristic of J(A) need not be four. This finding improves an important result appeared in Eur. J. Pure Appl. Math (2022).

Image segmentation is the process of partitioning an image into multiple segments where the goal is to simplify the representation of the image and make the image more meaningful and easier to analyze. In particular, semantic segmentation is an approach of detecting the classes of objects, based on each pixel. In the past, most semantic segmentation models were for only one single style, such as urban street views, medical images, or even Manga. In this paper, we proposed a semantic segmentation model called Dual-Pyramid Wide Residual Network (DPWRN) to solve the segmentation for cross-style datasets, which is suitable for diverse segmentation applications. The DPWRN integrated the Pyramid of Kernel paralleled with Dilation (PKD) and Multi-Feature Fusion (MFF) to improve the accuracy of segmentation. To evaluate the generalization of the DPWRN and its superiority over most state-of-the-art models, three datasets with completely different styles are tested in the experiments. Finally, the DPWRN verified its generalization and also showed its superiority over most state-of-the-art models.

One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. The state-of-the-art envisages various methods for estimating the claims reserve but without being applied to systems that do not present huge variability and heterogeneity, such as the healthcare sector. The methodology is based on generalized linear models using the Overdispersed Poisson distribution as shown in the state-of-the-art. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss-Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss-Newton method, where the choice of the initial guess is fundamental. The proposed methodology has been entirely developed using MATLAB and has been applied to estimate the claims reserve in the healthcare system of the Tuscany Region in Italy, as case study. The results obtained were validated by comparing them with the state-of-the-art by measuring the confidence intervals of the Overdispersed Poisson distribution parameters. Therefore local healthcare authorities could use the proposed and improved methodology to allocate the resources dedicated to healthcare and global management.

As a new type of gear, bevoloid gears have the advantages of compensating axial error, smooth transmission, and eliminating turning error, and they are widely used in applications requiring high transmission accuracy and stability. However, research on calculating the time-varying mesh stiffness of bevoloid gears is still limited, and there is an urgent need to propose a method that can calculate the stiffness of bevoloid gears quickly and accurately. This paper first establishes the bevoloid gear tooth profile expressions, then assumes a pair of bevoloid gears meshing with the same rack and derives the contact line equations of parallel axis bevoloid gear pairs, and analyze the contact process of bevoloid gears. We propose an analytical algorithm that uses the slicing method to calculate the stiffness of helical gears, straight bevoloid gears, and helical bevoloid gears, change the parameters of helical bevoloid gears respectively, and analyze the influence of different parameters on stiffness. Finally, the finite element method is used to verify the analytical method, and the correctness of the analytical calculation results is verified, and the errors are analyzed.

Thermal Non-destructive Testing and Evaluation (NDT&E) is crucial in ensuring the quality and safety of industrial materials, components, and structures. It serves as a key tool for assessing their operational reliability, thus enhancing safety in a wide range of industries. There is a growing demand for dependable, swift, remote, and secure inspection and assessment techniques to detect hidden flaws, especially for sustainable solutions prompts adjustments in design and manufacturing standards. Hidden defects often emerge during the service life of these materials and structures due to various stress factors, potentially resulting in catastrophic failures. This study delves into the optimal and dependable experimental method for conducting fast, remote, and secure inspections and assessments of carbon fiber reinforced polymer materials using Infrared Imaging (IRI) as part of Thermal Non-Destructive Testing and Evaluation (TNDT&E). Additionally, it examines the post-processing approach associated with this technique. This perspective also sheds light on the current state-of-the-art infrared imaging methods employed in TNDT&E, emphasizing their strengths and weaknesses about their ability to detect subsurface defects present within the material Most of the methods discussed in previous research primarily focus on the thermal differences in specific areas of a sample using processed thermal images, even though these images come from analyzing a series of images captured over time. This study highlights the latest research in thermal/infrared non-destructive testing and evaluation, along with the related post-processing techniques. It aims to not only show hidden subsurface defects through thermal differences also provides information about how these defects change over time.

Experimental studies of degradation of two ribonucleosides (guanosine and uridine) were carried out by making use of mechanochemistry. Mechanochemical experiments reveal the decomposition of guanosine and uridine promoted by nickel(II) and carbonate ions into guanine and uracil, respectively which were identified by HPLC and 1H NMR spectroscopy (this applied only to uracil). Additionally, DFT methodologies were used to probe the energetic viability of several degradation pathways, including in the presence of the same ions. Three mechanisms were analysed via ribose ring-opening: dry, single molecule water- and metal-assisted, which confirmed the mechanochemical degradation of ribonucleosides, with nucleobase preservation. These results can be applied to interpret the organic content of terrestrial and extra-terrestrial samples after mechanochemical treatment.

The analysis of water inflow is a key issue in the construction of subsea tunnels. However, present research on the water inflow of subsea tunnels is predominantly focused on single-bore tunnels. Given the current practices in engineering, it is common to see a parallel configuration of three bores. In such cases, the proximity of tunnels to one another might result in alterations to the water inflow due to the interplay of seepage fields. This paper begins by conducting a numerical simulation to assess the water inflow characteristics of a three-bore parallel subsea tunnel. The study then investigates the impact of many factors, including the permeability coefficient of the surrounding rock, the depth of the seawater, the depth of the tunnel, the spacing between tunnels, and the relative size of the tunnels, on the water inflow phenomenon. Furthermore, based on the principles of the analytic hierarchy process and fuzzy mathematics, a comprehensive assessment framework is developed to evaluate the water inflow of three-bore parallel subsea tunnels. Ultimately, this grade of water inflow is used in practical engineering. The results show that there is mutual influence between the three parallel tunnels, and the predicted water inflow will be too large if the single tunnel model is adopted. The interaction between tunnels should be considered in the water inflow grade evaluation system of the three-hole parallel submarine tunnel. Engineering cases show that using a single-hole tunnel model without considering the interaction between tunnels will lead to higher water inflow grades. The research results of this paper can provide guidance for the waterproofing and drainage design of three parallel subsea tunnels and the stability analysis of surrounding rocks.

In this study, we report the antibacterial mechanisms of action of uniform silver nanoparticles (AgNPs) and decorated activated carbon nanocomposite (CAC-AgNPs) obtained using a green synthesis approach. The nanomaterials were characterized by ultraviolet-visible (UV-vis) absorption spectra and Fourier transform infrared (FTIR) spectra. The antibacterial activity of the as-prepared nanomaterials was evaluated against an array of bacterial strains by microdilution method, whereas their cytotoxicity profile was evaluated on Vero cells (human mammalian cells). The antibacterial mechanistic studies of active nanomaterials were carried out through bacterial growth kinetics, nucleic acid leakage test, and catalase inhibition assay. A silver nanocomposite was successfully fabricated from Croton macrostachyus-based activated carbon. The as-prepared nanomaterials exhibited antibacterial activity against an array of bacterial strains (minimum inhibitory concentration (MIC) range: 62.5 to 500 µg/mL), the most susceptible being Escherichia coli and Staphylococcus aureus. Cytotoxicity studies of the nanomaterials on Vero cells revealed that the nanocomposite (median cytotoxic concentration (CC50): 213.6 µg/mL) was less toxic than the nanoparticles (CC50 value: 164.75 µg/mL) counterpart. Antibacterial mechanistic studies unveiled that the nanomaterials induced (i) bacteriostatic activity vis-à-vis E. coli and S. aureus and (ii) inhibition of catalase in these bacteria. This novel contribution on the antibacterial mechanisms of action of silver nanocomposite from C. macrostachyus-based activated carbon might contribute to the understanding of antibacterial action of these biomaterials. Nevertheless, more chemistry and in vivo experiments, as well as in depth antibacterial mechanistic studies are warranted for the successful utilization of these antibacterial biomaterials.

Neptunea cumingii is an economically important marine shellfish found in the Yellow Sea and Bohai Sea areas of China. However, due to environmental factors, such as global warming, Neptunea cumingii resources are in decline along the coast of China. In this study, samples of Neptunea cumingii were collected in Zhangzidao and Yantai during spring, summer， autumn, and winter to clarify the gene expression patterns and regulatory mechanisms in the gills in different seasons. Transcriptome analysis was conducted using Neptunea cumingii gill tissues and genes with significantly different expression levels were extracted for functional verification. The most genes with differences in expression（DEGs） were found in comparisons of the winter and summer samples. Gene enrichment analysis based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes terms showed that these DEGs were mainly involved in immune and metabolic pathways, and they had significant effects on oxidative stress, body metabolism, and protein synthesis in Neptunea cumingii. Further screening of DEGs identified 34 genes related to temperature regulation comprising 13 genes with roles in innate immunity in shellfish, 12 genes related to oxidative stress, and nine genes related to protein synthesis and energy metabolism. Eleven DEGs were randomly selected for qPCR verification and the results were consistent with the transcriptome analysis results. In summary, the transcriptome results differed significantly between seasons in the gill tissues of Neptunea cumingii. The expression levels of immune regulatory genes could be promoted in Neptunea cumingii during the high temperature season, whereas the expression of these genes may be inhibited in the low temperature season. The results obtained in this study provide insights into the molecular defense mechanisms that might allow Neptunea cumingii to adapt to climate change.

This study explores the implementation of an interdisciplinary curriculum in product and media design education and its impact on knowledge innovation and competency development. The curriculum is based on scaffolding theory, incorporating design proposals, workshops, digital design, marketing tests, and marketing activities guided by teachers and mentors from a branded ceramic workshop. The research findings demonstrate that the interdisciplinary curriculum facilitates knowledge innovation and fosters the development of students' professional skills, creativity, and problem-solving skills. The study also highlights the importance of stable scaffolding, including artefact, peer, and teacher support, which significantly contributes to cultivating transformational competencies, as outlined in the 2030 OECD Learning Compass. Additionally, the collaboration with Shanshing Four Seasons Celadon Studio on a patented ceramic product, "Funny Monkey," is a tangible example of the journey towards a triple-helix knowledge economy. This research underscores the importance of interdisciplinary curricula in promoting knowledge innovation and integrating transformational competencies in education. Scaffolding theory provides practical guidance for student learning and teaching strategies, presenting a sustainable roadmap for developing interdisciplinary curricula and offering a concrete and transferable pedagogical prototype for educational innovation.

Track fasteners play a pivotal role in infrastructure inspection for high-speed rail. Yet, images taken by drones often capture shadows cast by electrical towers flanking the high-speed rail tracks. These shadows can hinder the visibility of the track fasteners, thereby impacting detection efficiency and accuracy considerably. The present paper introduces an end-to-end shadow removal algorithm, rooted in generative adversarial network training. The comprehensive network framework is segmented into three sub-networks: pseudo-mask generation, shadow removal, and result refinement. We have integrated a Fourier convolutional residual module to bolster the feature extraction capability of the generator network. This integration ensures the network retains a global receptive field, even in its more superficial layers. By employing an overall weighted loss function, we enhance the quality of the images produced without shadows. Further, a perceptual loss function has been incorporated to retain the structural information of objects, setting the stage for subsequent defect detection. Our results highlight that Pse-ShadowNet adeptly eradicates fastener shadows while maintaining vital visual features, including object position, structure, texture, edges, and other key visual elements. Consequently, the reconstructed images are detailed and showcase superior image quality.

The transition of the energy system to renewable sources means the cheap energy storage in fossil fuels must be replaced by other options. A large part of the demand for useful energy is heat and cold, often produced from electric energy by a resistance heater, compression heat pump or cooler. The question is then to store the initial electric energy by electric energy storage (EES) or the useful energy by thermal energy storage (TES). In a desktop study both options were compared, by the choice made in existing applications, and also generally analyzing current technology data. For the latter, cost, round-trip efficiencies, life cycles and life time of EES, specifically for batteries, and of TES, specifically for hot and cold water, ice and other PCM were collected. Applications studied are heating and cooling in buildings and in industry. Application-typical conversion efficiencies were also collected and taken into account. The results show that in many existing installations TES, incl. by PCM, is already preferred, and that TES is advantageous in most investigated applications economically, in addition to technical advantages. Thus, TES has a large potential in the transition of the energy system to stabilize the electricity grid by demand side management.

Drug delivery through Liposomes has shown tremendous potential in terms of the therapeutic application of nanoparticles. There are several drug-loaded liposomal formulations approved for clinical use that help mitigate harmful effects of life-threatening diseases. Developments in the field of liposomal formulations and drug delivery have made it possible for clinicians and researchers to find therapeutic solutions for complicated medical conditions. A key aspect in the development of drug-loaded liposomes is a careful review of optimization techniques to improve the overall formulation stability and efficacy. Optimization studies help in improving/modulating the various properties of drug-loaded liposomes and are vital for the development of this class of delivery systems. A comprehensive overview of the various process variables and factors involved in the optimization of drug-loaded liposomes is presented in this review. The influence of different independent variables on drug release and loading properties with the application of a statistical experimental design is also explained in this article.

Chronic traumatic encephalopathy (CTE) is a unique neurodegenerative disease that is associated with repetitive head impacts (RHI) in both civilian and military settings. In 2014, the research criteria for the clinical manifestation of CTE, traumatic encephalopathy syndrome (TES) were proposed to improve the clinical identification and understanding of the complex neuropathological phenomena underlying CTE. This review provides a comprehensive overview of the current understanding of the neuropathological and clinical features of CTE, proposed biomarkers of traumatic brain injury (TBI) in both research and clinical settings, and a range of treatments based on previous preclinical and clinical research studies. Due to the heterogeneity of TBI, there is no universally agreed-upon serum, CSF, or neuroimaging marker for its diagnosis. However, as our understanding of this complex disease continues to evolve, it is likely that there will be more robust, early diagnostic methods and effective clinical treatments. This is especially important given the increasing evidence of a correlation between TBI and neurodegenerative conditions, such as Alzheimer's disease and CTE¹. As public awareness of these conditions grows, it is imperative to prioritize both basic and clinical research, as well as the implementation of necessary safe and preventative measures.

Background: Two types of medicinal systems are available in Korea: Western and oriental. These exist as separate services that independently provide medical care to patients. We determined the utility and benefits of compressive and integrated medical services (CIMS) comprising 12 sessions of acupuncture and healing programs over 6 weeks. Methods: In this two-group parallel single-center randomized controlled assessor-blinded trial, 24 participants were assigned to either the experimental (conventional medical treatment plus CIMS, n = 12) or control (conventional medical treatment, n = 13) group. Spinal nerve root block was performed on the compressed spinal nerve root (identified using magnetic resonance imaging) when no improvement was observed after the initial treatment. The experimental group received 12 cycles of acupuncture and manual therapy for 6 weeks; the control group received conventional medical treatment alone. Results: The average age of participants in the experimental and control groups was 70.73 ± 5.95 and 67.33 ± 8.89 years, respectively. There were no significant differences between the groups in terms of age, body mass index, Leeds Assessment of Neuropathic Symptoms and Signs, sex, and current medical history. We found high compliance for both programs (acupuncture and healing). On exclusion of between-group effects, the visual analog scale (VAS) score improved significantly over time (p = 0.045). Further, comparison of the groups after excluding the effects of visits revealed significantly lower VAS scores in the experimental group than in the control group (p = 0.000). Conclusions: Patients with degenerative lumbar spinal stenosis who mainly complain of radiating pain in the lower leg may benefit from CIMS after spinal nerve root block for ≤3 months after treatment. Our study findings suggest that this treatment improves spinal function and Oswestry Disability Index score. However, CIMS did not improve QoL.

Transfer learning has proven to be a valuable technique for deploying machine learning models on edge devices and embedded systems. By leveraging pre-trained models and fine-tuning them on specific tasks, practitioners can effectively adapt existing models to the constraints and requirements of their application. In the process of adapting an existing model, a practitioner may make adjustments to the model architecture, including the input layers, output layers, and intermediate layers. In this study, we examine the effects of these adjustments on the runtime and energy performance of an edge processor performing inferences. Based on our observations, we make recommendations for how to adjust convolutional neural networks during transfer learning to maintain runtime performance. We observe that the Edge TPU is generally more efficient than a CPU at performing inferences on convolutional neural networks, and continues to outperform a CPU as the depth and width of the convolutional network increases. We explore multiple strategies for adjusting the input and output layers of an existing model and demonstrate important performance cliffs for practitioners to consider when modifying a convolutional neural network model.

The basis for computation in the brain is the quantum threshold of the ‘soliton’ accompanying the ion changes of the action potential and the refractory membrane at convergences. We provide a logical explanation from the action potential to a neuronal model of the coding and computation of the retina and have explained how the visual cortex operates by quantum phase processing. In the small world network parallel frequencies collide into definable patterns of distinct objects. Elsewhere we have shown how many sensory cells are mean sampled to a single neuron and that convergences of neurons are common. We have also demonstrated, using the threshold and refractory period of a quantum phase pulse, that action potentials diffract across a neural network due to the annulment of parallel collisions in phase ternary computation (PTC). Thus, PTC applied to neuron convergences results in collective mean sampled frequency and is the only mathematical solution within the constraints of brain neural networks (BNN). In the retina and other sensory areas, we discuss how this information is coded and then understood in terms of network abstracts within the lateral geniculate nucleus (LGN) and visual cortex. First by defined neural patterning within a neural network, and then in terms of contextual networks, we demonstrate that the output of frequencies from the visual cortex contain information amounting to abstract representations of objects in increasing detail. We show that nerve tracts from the LGN provide time synchronisation to the neocortex (defined as the location of the combination of connections of the visual cortex, motor cortex, auditory cortex, etc). The full image is therefore combined in the neocortex with other sensory modalities so that it receives information about the object from the eye, and all abstracts that make up the object. Spatial patterns in the visual cortex are formed from individual patterns illuminating the retina and memory is encoded by reverberatory loops of computational actions potentials (CAPs). We demonstrate that a similar process of PTC may take place in the cochlea and associated ganglia, as well as ascending information from the spinal cord, and that this function should be considered universal where convergences of neurons occur.

Young dementia carers (YDCs) rarely receive appropriate training and support. Their visibility and identification remain dangerously low and as a consequence, support initiatives being developed are failing to reach them. This study explored the success (or failure) of YDCs identification pathways as well as the barriers and enablers to their implementation. An explorative qualitative approach was followed drawing on the experiences of parents of YDCs, dementia researchers, professionals in the field of dementia/young carers and young adult carers. Data collection involved semi-structured interviews (n=17) and a participatory 2-hour workshop to discuss and critique preliminary themes as well as explore strategies to increase the visibility and identification of YDCs. Five themes were identified: A ‘whole family approach’ (as a pathway to identification); ‘Not a carer’ (self/family identification); A postcode lottery (high variability of support services); Tailored support that is ‘fit for purpose’ and the ‘power’ of peer support. Recommendations on potential initiatives and actions that can help raise awareness and increase identification success of YDCs are proposed. Our findings support the need for a broad and holistic approach to the identification of YDCs that runs alongside the development of support initiatives that are accessible and relatable. The support itself will play a role in improving subsequent identification or hindering it if not ‘fit for purpose’.

Background: The red swamp crayfish Procambarus clarkii (Girard, 1852), native to the United States and northern Mexico, has been introduced in many countries worldwide. The species has a remarkable invasiveness, due to its high reproductive rate and to its ability to tolerate broad ranges of water parameters like salinity. Nevertheless, no consistent observations of P. clarkii have been reported in the marine environment until now. Methods: Through the observations of a personal acquaintance and of two volunteers who published photos of the species on Facebook marine biology and fishing groups, we collected data showing the presence of the crayfish within marine ecosystems. Results: P. clarkii has been spotted in three different localities, in two of which observations are recurrent. The species can live miles from the nearest freshwater stream, and can colonise the depths up to 20 meters. Conclusions: The multiple observations of P. clarkii collected in our study uncover a persistent presence of the species within some marine areas, rather than its mere sporadic occurrence. As resistance to increasing salinities is influenced by multiple genes, genetic admixture between genetically-differentiated individuals coming from distinct geographical origins could have brought together different salinity-resistance genes, leading to the formation of these resilient phenotypes.

Staphylococcus aureus is a causative agent of nosocomial infections and its antibiotic-resistant strains are concerning. Solutions are being explored to improve the treatment of these infections, including repositioning drugs such as statins and using nanoparticles with antimicrobial properties. This study aimed to evaluate the antimicrobial effects of simvastatin (SIM) and biologically synthesized silver nanoparticles (bio-AgNPs) in isolate form and in combination by assays of minimum inhibitory concentration (MIC), an in vitro biofilm model, and the association of antimicrobials against clinical strains of S. aureus. Bio-AgNPs showed a 53.8 ± 1.23-nm mean diameter and standard deviation, a 0.23 polydispersity index, and a −25.66 ± 2.19-mV mean potential and standard deviation. Transmission electron microscopy confirmed the formation of nanoparticles and the presence of Ag0 and AgCl. S. aureus strains were sensitive to bio-AgNPs and SIM, showing 31.88-187.5 and 74.66-149.32 μM concentrations, respectively. Our association assay showed 2.0 fractional inhibitory concentration indices (i.e., indifferent for clinical strains) and 0.32 values for the standard ATCC 29213 strain (synergy). Our biofilm inhibition assays with isolated SIM and bio-AgNPs showed decreased biofilm formation from 4× to ⅛ MICs, showing no synergism in association. These findings evince that simvastatin and bio-AgNPs at subinhibitory concentrations can serve as antimicrobial agents against S. aureus biofilm.

The α-synucleinopathies are a group of neurodegenerative diseases characterized by the deposition of α-synuclein aggregates (α-syn) in the brain. Currently, there is no suitable tracer to enable a definitive early diagnosis of these diseases. We reported candidates based on 4,4'-disarylbisthiazole (DABTA) scaffold with a high affinity towards α-syn and excellent selectivity over Aβ and tau fibrils. Based on prior in silico studies a focused library of 23 halide-containing and O-methylated DABTAs was prepared. The DABTAs were synthesized via a modified two-step Hantzsch thiazole synthesis, characterized, and used in competitive binding assays against [3H]PiB, and [3H]DCVJ. The DABTAs gave an overall chemical yield of 15 – 71%, with a calculated lipophilicity of 2.5 – 5.7. The ligands demonstrated an excellent affinity to α-syn with both [3H]PiB and [3H]DCVJ: Ki 0.1 – 4.9 nM and up to 20 – 3900-fold selectivity over Aβ and tau fibrils. It could be concluded that in-silico simulation is useful for the rational design of a new generation of DABTAs. Further investigation of the leads in the next step is encouraged: radiolabeling of the ligands with radioisotopes such as fluorine-18 or carbon-11 for in vivo, ex vivo and translational research and for further in vitro experiments on human-derived protein aggregates.

Assessment of renal function is critical to diagnose and manage renal age-related decline, disease (KD), and failure, which are prevalent in the elderly population. Glomerular filtration rate (GFR) is widely used as an indicator of kidney function, but its direct measurement is challenging, as are its age and gender caveats. This makes difficult the differential diagnosis, between age-related physiological decline and KD and/or failure. Currently, the inflammation-based modified Glasgow prognostic score (mGPS) is emerging as promising biomarker of several inflammatory acute/chronic diseases. In this study, the large variability of eGFR with age and gender was evaluated, as the association of eGFR values with mGPS levels. A population of 57449 adult participants (age ≥18 years) was enrolled. Appropriate circulating biomarkers were measured to detect eGFR and mGPS values. The data obtained demonstrated a significant decrease of eGFR in men vs. women across the four selected age classes (18-40, 40-60, 60-80, 80-100 years), eGFR classes were significantly associated with mGPS (p<0.001), as age-classes and gender with mGPS categories. Accordingly, the percentage of people having a mGPS score= 2 significantly increased across the eGFR classes: with a 11% in the G1/eGFR class to achieve the 44% in G5/eGFR. Thus, the combination of mGPS with eGFR could represent the best benchmark risk model for differentiating age-related renal decline from a very KD, and for a better identification of different degrees of KD severity.

Chromium poisoning strongly influences the performance of solid oxide fuel cell (SOFC) stacks. A three-dimensional, time-dependent, computational fluid dynamics model of a single channel of a F10 SOFC stack in Forschungszentrum Jülich GmbH is developed to investigate chromium poisoning for different stack designs, temperatures and absolute air humidities. The model takes into account both chemical and electrochemical mechanisms of chromium poisoning and is able to predict the spatial distribution of SrCrO4 and Cr2O3. The voltage degradation over 100 kh can be simulated quantitatively. According to the simulation results, chromium poisoning is almost eliminated by the application of the protective coating fabricated by the atmospheric plasma-spraying (APS) technology. Besides, with the help of the APS protective coating, operating a SOFC stack with less dehumidified air at 650 oC is possible according to the simulation.

The maritime economy is at the forefront of unprecedented sustainability challenges. Addressing ecological externalities in port operations supports the decarbonization goals of the United Nations (UN) Climate Action program and port city transition towards resilient and sustainable urban units. This research brings out an empirical assessment of seaport performance from an eco-environmental point of sustainability with a non-parametric analysis. Most common indicators from the cross-sectoral Global Reporting Initiative (GRI) database for 21 world’s busiest seaports are used for the analysis. This research integrates four different models with inputs: CO2 emission, electricity consumption, waste, and water consumption, and; outputs: employee, revenue, and container throughput. Projection pathways are established for inefficient seaports to improve sustainability performance. The analysis shows that the seaports of Qingdao and Cartagena as the most sustainably performing seaports under the selected maritime sustainability indicators. This research supports port managers in understanding the strengths and weaknesses of their operations and helps frame strategic policies toward achieving overall sustainability in the maritime industry across SDG 14 (marine ecosystem) and SDG 13 (Climate mitigation) goals of the 2030 Urban Agenda.

This paper presents the tensor properties of joint probability densities on a Riemannian manifold. Initially we develop a binary data matrix to record variables' values of a large number of particles confining in a closed system at a certain time in order to retrieve the joint probability densities of related variables. By introducing the particle oriented coordinate and generalized inner product as a multi-linear operation on the basis of this coordinate, we extract the set of joint probabilities and prove them to meet covariant tensor properties on a general Riemannian space of variables. Based on the Taylor expansion of scalar field in Riemannian manifolds, it has been shown that the symmetrized iterative covariant derivatives of cumulative probability function defined on Riemannian manifold also gives the set of related joint probability densities equivalent to the aforementioned multi-linear method. We show these covariant tensors reduce to classical ordinary partial derivatives in ordinary Euclidean space with Cartesian coordinates and gives the formal definition of joint probabilities by partial derivatives of cumulative distribution function. The equivalence between symmetrized covariant derivative and generalized inner product has been concluded. Some examples of well-known physical tensors clarify that many deterministic physical variables are presented as tensor densities with an interpretation similar to probability densities.

A traditionally salient topic of empirical investigation in the sociology of religion, this paper seeks to offer a recent investigation into the intergenerational transmission of religion and the parental forms of religious engagement that predict adult engagement with religion. The study of this paper explores the intergenerational transmission of religion, focusing on the parental forms of religious identity and engagement that influence religious identity, beliefs, and practices in adulthood. By analyzing the 2018 GSS dataset in the United States with multiple regression analyses, I find strong parental and childhood influences on adult religiosity, religious service attendance, and belief in God. Indeed, this engagement often mirrors parental engagement for these variables. However, while paternal religious identity often predicts these religious variables, I find that the religious identity and engagement of parents generally does not predict religious identity in adulthood. Ultimately, while these results generally show strong predictive mechanisms of intergenerational transmission, they also illustrate that these relationships are variably dependent on the form of parental and adult religious engagement and which parent participates or is associated with that engagement.

Quantum computing is an emerging field that have given a significant impact on optimization. Among the diverse quantum algorithm, the quantum gradient descent has become a prominent technique for solving unconstrained optimization problems. In this paper, we propose a quantum-spectral Polak-Ribi{\'e}re-Polyak (PRP) conjugate gradient approach. The technique is considered as a generalization of the spectral PRP method which employs a $q$-gradient that approximate the classical gradient with quadratically better dependence on the quantum variable $q$. Additionally, the proposed method reduces to the classical variant as quantum variable $q$ gets closer to $1$. The quantum search direction always satisfies the sufficient descent condition and does not depend on any line search. This approach is globally convergent with the standard Wolfe line search without any convexity assumption. Numerical experiments are conducted and compared with the existing approach to demonstrate the improvement of the proposed strategy.

This systematic literature review explores the burgeoning use of Artificial Intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. In this landscape, AI is crucial in addressing the complexity and dynamism of contemporary manufacturing processes, including machine breakdowns, fluctuating orders and unpredictable job arrivals. This systematic literature review, conducted using the Scopus database and bibliometric tools, pursues two primary objectives. First, it identifies the prevailing trends in solving scheduling problems with AI and identifies the most commonly used AI techniques in the literature. Secondly, it analyses how authors have successfully employed AI to address production scheduling challenges in real-world industrial settings and assesses the benefits obtained by companies. The dynamic nature of manufacturing systems requires adaptive scheduling paradigms. AI, including Particle Swarm Optimization, Neural Networks, and Reinforcement Learning, is applied to optimize production processes, predict machine failures, and achieve substantial benefits. In real-world applications, these AI-driven solutions have led to reduced production costs, enhanced energy efficiency, and more efficient scheduling processes. AI is increasingly recognized as an essential tool in addressing the evolving challenges of modern manufacturing environments.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence and structural knowledge of drugs, targets, and pockets using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model’s performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, the supremacy of S2DTA became strikingly apparent. It showcased a noteworthy 25.2% reduction in Mean Absolute Error (MAE) and an impressive 20.1% decrease in Root Mean Square Error (RMSE). Furthermore, S2DTA exhibited substantial advancements in other pivotal metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2. These metrics experienced remarkable increments of at least 19.6%, 17.5%, 8.1%, and a remarkable 49.4%, respectively. Finally, we conducted interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism, fully proving that S2DTA is a valuable and accurate tool for predicting DTA. For further exploration, the source data and code repository can be accessed at https://github.com/dldxzx/S2DTA.

Manufactured sand and natural sand have great differences in terms of physical properties, chemical composition, and so on. In the mix ratio design of manufactured sand concrete, the traditional common concrete ratio cannot be used. The water-cement ratio playing a crucial role in the mix ratio design. The optimum water-cement ratio can not only improve concrete performance, it can also reduce cement consumption and costs. The present study examines the working performance indicators and mechanical performance indicators of manufactured sand concrete using Fujian granite with three strength grades (C35, C40 and C50) at different water-cement ratios. The impact of water-cement ratio on the operational and mechanical performance of manufactured sand concrete with different strength grades is analyzed and summarized. Revised sentence: The findings suggest that the workability of Fujian granite concrete is directly influenced by the water-cement ratio. Additionally, the water-cement ratio indirectly affects the mechanical properties of manufactured sand concrete. Finally, when changing the water-cement ratio, cube compressive strength, splitting tensile strength and bending strength exhibit similar trends. The optimal water-cement ratio for C35 concrete is determined to be 0.39, considering the workability and mechanical properties of Fujian granite manufactured sand concrete. Similarly, the optimal water-cement ratio for C40 concrete is 0.34, while for C50 concrete it is 0.32.

General anesthesia and surgical stress can suppress the immunological response by acting both directly on the immune system and indirectly on the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. Disturbance of the immune system during the perioperative period can lead to complications such as wound-healing disorders and infections up to sepsis. Effective-ness of acupuncture in regulating the immune function by increasing leukocyte numbers and in-hibiting inflammatory response has been proven. The aim of this study was to explore the impact of electroacupuncture (EAP) on the dynamic bal-ance of the immune system and immune cell populations in dogs undergoing surgery. For this purpose, twelve healthy bitches scheduled for elective ovariectomy were divided into two groups according to whether (EAP, n=6) or not (CTR, n=6) a peri-operative electroacupuncture treatment was performed. Levels of leukocytes (neutrophils, monocytes, T- and B-cells) and immunoglobu-lins M (IgM) and A (IgA) were measured in blood samples collected before (T0), 1 hour (T1) and 2.5 hours (T2) after anesthesia induction. Leukocytes count decreased from T0 to T1 in both groups and restored within 1.5 hours in EAP group whereas remained significantly lower in CTR group (P<0.02). In particular, neutrophils and monocytes increased in dogs receiving EAP (P<0.01) while T-cells decreased in CTR group (P<0.04) at T2. B-cells and cytotoxic T-cells de-creased in EAP dogs (P<0.04) at T2. No differences in helper T-cells, IgM and IgA levels were recorded between groups and over time. Our results suggest a modulatory effect of EAP on the immune system which is early expressed on neutrophils, monocytes and T-cells.

This paper delves into the multifaceted domain of the aerospace industry, examining its evolution, current challenges, and imperative focus on quality management and process integration. The aerospace sector, driven by technological advancements and a burgeoning global demand for air travel and freight transport, necessitates a thorough analysis of its industrial fabric and operational intricacies. This research endeavors to analyze the dynamics of the aerospace industry, pinpoint its challenges, and propose an integrated approach to enhance efficiency, quality, and sustainability. The primary goals encompass understanding the evolving industry landscape, identifying critical challenges, and offering innovative solutions by amalgamating principles of Industry 4.0 into quality management and processes within the aerospace sector. Through an in-depth exploration of various facets, this research underscores the pivotal role of efficient processes and integrated quality management in achieving sustainable growth and competitiveness in the aerospace industry. By aligning with the paradigm of Industry 4.0, organizations can optimize their operations and contribute to the industry's advancement, delivering safer and more cost-effective aerospace products. The study adopts a multifaceted approach, incorporating extensive literature review, critical analysis of industry trends, examination of quality management frameworks, and a thorough evaluation of the integration potential of Industry 4.0 technologies. The research also involves case studies and expert insights to validate the proposed approach. The investigation reveals that by leveraging Industry 4.0 technologies and embracing an integrated approach to quality management, the aerospace industry can significantly enhance operational efficiency, product quality, and overall sustainability. The seamless integration of processes and the implementation of advanced quality frameworks pave the way for a more competitive and future-ready aerospace industry, meeting the evolving demands of a globalized world.

Laser marking on polymer composite surfaces can be difficult to contrast and cause readability problems for electronic decoding equipment on production lines due to poor interaction between the laser and the fibres used to reinforce these materials. This problem can be solved with the right choice of marking parameters, resulting in savings for companies by avoiding production problems such as rejection, scrap and customer complaints. The present work uses the PBT/glass fibre composite used in the manufacture of instrument panels for motorcycles. The tests were carried out with different laser marking parameters using a Nd: YAG laser. Subsequently, the laser-marked DMC codes were analysed using a Verifier to evaluate the quality according to the ISO/IEC 29158:2020 standard. A detailed analysis of these surfaces was also carried out to observe some physical and chemical changes using SEM microscopy and EDS spectroscopy. The optical analysis showed that the lower the radiation power and pulse frequency and the higher the marking speed, the weaker the laser marking and therefore the poorer the DMC code quality, which was confirmed by the SEM. EDS spectroscopy showed that the laser marking process did not affect the chemical changes on the sample surface.

Due to its distinctive chemical and biological properties, pectin has recently drawn much attention in biomedical and tissue engineering applications. Polymers like pectin with cell-instructive properties are attractive natural biomaterials for tissue repair and regeneration. Besides, bioactive pectin and pectin-based composites exhibit improved characteristics to deliver active molecules. Pectin and pectin-based composites serve as interactive matrices or scaffolds by stimulating cell adhesion and cell proliferation and enhancing tissue remodeling by forming an extracellular matrix in vivo. Several bioactive properties, such as immunoregulatory, antibacterial, anti-inflammatory, anti-tumor, and antioxidant activities, contribute to the pectin's and pectin-based composite's enhanced applications in tissue engineering and drug delivery systems.This paper reviews the promising characteristics of pectin or pectic polysaccharides and highlights its potential applications in drug delivery and tissue engineering. Tissue engineering scaffolds containing pectin and pectin-based conjugates or composites demonstrate essential features such as non-toxicity, tunable mechanical properties, biodegradability, and suitable surface properties. The design and fabrication of pectic composites are versatile for tissue engineering and drug delivery applications.

Background Recent studies indicate that donor innate immune responses participate in initiating and accelerating innate responses and allorecognition in the recipient. These immune responses negatively affect recipient outcomes and predispose recipients to allograft coronary artery vasculopathy (CAV). We hypothesized that donor cause of death (COD) associated with higher levels of innate immune response would predispose recipients to more adverse outcomes post-transplant, including CAV. Methods We performed a single-institution retrospective analysis comparing donor characteristics and COD to recipient adverse cardiovascular outcomes. We analyzed the medical records of local adult donors (age 18-64) in a database of donors where adequate data was available. We linked donor characteristics and COD to recipient adverse cardiac outcomes after transplant, including cardiovascular (CV) death and incidence of significant antibody-mediated rejection (pAMR) after transplant (>2 episodes of pAMR within 90 days post-transplant). The data were analyzed using logistic regression, log-rank test of differences, and Tukey Contrast. Results Donor advanced age, female sex, and CODs of motor vehicle accident and intracranial hemorrhage were significantly associated with a higher incidence of recipient CV death and early pAMR compared to other donor characteristics and CODs. Conclusions In this single institution study, we found that recipients with hearts from donors over 40 years, donors who were female, or donors who died with a COD of motor vehicle accidents or intracranial hemorrhage had a higher frequency of CV related deaths and early pAMR. Donor monitoring and potential treatment of innate immune activation may decrease subsequent recipient innate responses and allorecognition stimulated by donor derived inflammatory signaling which lead to adverse outcomes.

Guiding the visitor to appreciate the sights during an excursion is crucial to building a memorable experience. In the literature, we find many articles exploring the use of sophisticated technologies towards such a goal. The case of tourism proposals for inland areas exhibits a noteworthy aspect, since the presence of human artifacts and the signage may damage the experience, and introduce pollutants as well. This paper analyzes the signage for a natural trail putting into practice a holistic methodology that takes into account all aspects of the problem, starting from the touristic vocation of the area and the social fabric. The analysis indicates slow, community-involved tourism as the preferable target, with extreme requirements of sustainability in the social, economic, and environmental domains for the signage solution. Appropriate governance guidelines are given to implement the proposal. Footprint evaluations address the QR-code technology, which is used to design a real-scale solution finally deployed on the field.

The base excitation of mechanical equipment can result in pipeline system vibration which seriously affects the safe operation of the equipment. Particle damping technology is based on the particle collision dissipation principle to realize the structural vibration energy dissipation. This paper introduces the particle damping method into the vibration control of pipeline system. A particle damper has been designed for the structural characteristics of the pipeline. The influence of the material and structure of the pipeline particle damper, the external excitation and other parameters on the energy dissipation characteristics of particle damping are revealed. The energy dissipation mechanism of the pipeline particle collision damper under various parameter is analyzed. A pipeline vibration reduction test system with particle damping has been established. The effects of different excitation conditions, damper materials and number of unit structures of particle damper on the vibration damping effect of pipeline systems are investigated comparatively. The influence of damper parameters on the vibration suppression of pipeline system is obtained. The research in this paper can provide technical references for vibration suppression of pipeline systems.

The spontaneous combustion of coal in goaf is the main cause of mine fire at present. Lignite has become a major hidden danger in mine production safety because of its easy oxidation. In this paper, an innovative strategy was proposed to inhibit the spontaneous combustion of lignite by using the principle of microbial-induced calcium deposition. Based on the optimized culture method, a novel composite inhibitor of Bacillus pasteurelli was prepared. SEM, pore size analysis and FT-IR experiments were carried out simultaneously, and the oxidation properties of lignite before and after inhibition were quantitatively characterized from the perspective of microstructure, and the flame retardant properties of microbial and chemical retardants on coal samples were compared. The results show that a large number of deposited calcium carbonate particles are obviously attached to the surface of lignite after microbial inhibition treatment, which plays a physical oxygen insulation role. At the same time, the total pore volume and specific surface area of the coal sample decreased by 68.49% and 74.01%, respectively, indicating that microbial inhibitors can effectively plug the primary pores of lignite. Based on the peak measurement of 400-4000cm-1 infrared spectrum of coal samples, it is found that the contents of active groups including hydroxyl, carboxyl and methyl/methylene in lignite molecules after microbial inhibition are lower than those in raw coal, especially the methyl/methylene content involved in the initial oxidation reaction decreased by 96.5% compared with the baseline content in raw coal. The results show that the oxidation and self-heating capacity of lignite after microbial inhibition is effectively restrained in the initial spontaneous combustion. The research results of this paper can provide effective solutions for the prevention and control of coal spontaneous combustion risk in mined-out area.

Drafting is a common technique to reduce the drag experienced by elite runners on races, leading to better running efficiency and energy savings. The tactic has been successfully used in previous races where world records were set, especially by Eliud Kipchoge in the 2018 and 2022 editions of the Berlin marathon. In the 2023 edition of the Chicago Marathon, Kenyan runner Kelvin Kiptum broke the Marathon record after a 2:00:35 finish. This feat becomes even more impressive by the lack of use of drafting formations, despite the availability of two pacers for the majority of the race. In this study, the drag faced by Kiptum and his pacers during the race is calculated by means of computational fluid dynamics (CFD). The performance of each runner is evaluated from an energetic standpoint, and the analysis is extended to include more efficient drafting formations. Our results indicate that, by properly using the advantages of drafting, Kiptum could have finished the race at a staggering 1:57:34, a full three minutes better than his own record and 215 seconds better than the previous world record.

In this review, we will cover a general overview of the current panorama on lncRNAs with an actual or potential role as biological markers in cancer. We will discuss examples of multi-omics approaches that integrates information on somatic aberrations, gene expression and epigenomics, with the scope of providing a more comprehensive view of the functional impact of lncRNA profiles and how these paradigm can be exploited for the discovery and selection of lncRNAs with a functional role and their use as variables informing on progression and prognostic and help guiding the selection of therapeutic strategies for cancers. Finally, we propose a perspective for future evolution of the study of lncRNAs and the discovery of their functional and harnessing their potential to assist in clinical management of malignancies.

Alzheimer's Disease (AD) is a neurodegenerative disease that is common in the elderly. This paper introduces the overview of Alzheimer's disease and the application of relevant signal processing methods in its detection. Signal processing is a technique that converts raw data into meaningful information and is widely used in the medical field. This paper lists common signal processing techniques, including Fourier transform, time-frequency analysis and statistical signal processing, and discusses their applications in the detection of Alzheimer's disease. Fourier transform can convert time domain signals into frequency domain representations, providing an effective tool for the study of EEG in Alzheimer's disease. Time-frequency analysis can perform a combined time and frequency analysis of the signal to help detect the signal characteristics of Alzheimer's disease. Statistical signal processing methods can be used to identify the features of Alzheimer's disease by building mathematical models. Finally, the challenges of Alzheimer's disease detection are discussed, including signal noise, diversity, and insufficient data volume. Through in-depth research and development of signal processing methods, it is expected to improve the accuracy and efficiency of early detection of Alzheimer's disease.