Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova's (SoTa), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova's, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

There are rich proper noun derivative semantic relationships in geographic entity vector data. The identification of traditional proper noun derivative semantic relationships usually requires toponym researchers to examine the source of derived toponyms by consulting a large number of toponym documents. This method cannot adapt to the mining of proper noun derivative semantic relationships for large-scale geographical entity. In this regard, this paper proposes a method of identifying the proper noun derivative semantic relationships for geographical entity by combining prompt learning and XGBoost model. This method transforms the extraction problem of proper noun derivative semantic relationships into a binary classification problem by analyzing the characteristics of the derivation relationship of toponyms in the form of toponyms and the spatial and temporal distribution of geography. Firstly, prompt learning and gaussian mixture model are used to extract and cluster the derivation words of toponyms, so as to realize the recognition of derived toponyms collections. Then, enumerate the possible proper noun derivations in the toponym set, and then the XGBoost model is used to identify the semantic relations derived from the proper noun of geographical entities. In the experiment, the recognition method of proper noun derivative semantic relationships based on prompt learning and XGBoost proposed in this paper has achieved good relationship recognition effect in the experiment. This method has certain practical value in the fields of toponym management and toponym translation.

The modern textbook analysis of the thermal state of photons inside a three-dimensional reflective cavity is based on the three quantum numbers that characterize photon’s energy eigenvalues coming out when the boundary conditions are imposed. The crucial passage from the quantum numbers to the continuous frequency is operated by introducing a three dimensional continuous version of the three discrete quantum numbers, that leads to the energy spectral density and to the entropy spectral density. This standard analysis obscures the role of the multiplicity of energy eigenvalues associated to the same eigenfrequency. In this paper we review the past derivations of Bose’s entropy spectral density and present a new analysis of energy spectral density and entropy spectral density based on the multiplicity of energy eigenvalues. Our analysis explicitly defines the eigenfrequency distribution of energy and entropy and uses it as a starting point for the passage from the discrete eigenfrequencies to the continuous frequency.

The purpose of this work is to describe and explain the characteristics of pedagogy as a science of education, as an object, concepts and research method. The methodology assumed was the review of the history of pedagogy, especially the classics of pedagogy, those who built it with their experiences in laboratory schools. Here are listed the main concepts of pedagogy, its laws and principles; she establishes the opposition that exists between teaching-learning and education as formation of the personality; she also delimits the fields of the pedagogical method, of didactics or teaching-learning, and the methodology of educational labor that organizes the process and the educational context, the regime and the lifestyle, such as those experienced in the "colonies" or "communes" of children and adolescents, organized and directed by Neill and Makarenko. Since the present article does not derive from a primary source or from field work, as an empirical or experimental research, the results constitute the content of the article, as conclusions.

MnO₂ nanoparticles are considered nanozymes with intrinsic peroxidase-like activity and there are several reports on their application in the field of nanozyme-based sensing and detection, however, up to now, there is no report on the investigation of their biochemical and kinetics properties toward enzyme-mediated oxidations. Hence, in this work, the MnO₂ nanozymes were synthesized and characterized with different characterization methods. Thereafter, their biochemical properties including pH stability, thermal stability, and salt stability as well as their kinetics perfromances were evaluated toward n-electron irreversible oxidation of 3,3’-diaminobezedine. The results showed that the as-prepared nanozymes reveal their maximal enzyme-like activity over a wide pH range of 3.0-6.0 at a temperature of 23-25 ℃ toward oxidation of 3,3’-diaminobezedine. Besides, the salt stability studies exhibited that the as-mentioned nanozymes can save their maximal activity over a wide range of high salt concentrations over 3-7 M of NaCl. Moreover, the kinetics studies revealed a K_m as low as 1.6 mM and a V_max as high as 47 nM sec^-1 for the MnO₂ nanozymes toward irreversible oxidation of DAB to produce the brown-colored polyDAB.

RNA viruses exhibit a great tendency to mutate. Starting from 2019, the wild type that was labeled Hu-1 has during the last 3 years evolved to produce several dozen new variants, as a consequence of mutations. Mutations cause changes in empirical formulas of new virus strains, which lead to change in thermodynamic properties of biosynthesis and binding. These changes cause changes in the rate of reactions of binding of virus antigen to the host cell receptor and the rate of virus multiplication in the host cell. Changes in thermodynamic and kinetic parameters lead to changes in biological parameters of infectivity and pathogenicity. SARS-CoV-2 has starting from in 2019, until today, evolved towards increase in infectivity and maintaining constant pathogenicity, or for some variants a slight decrease in pathogenicity. In the case of Omicron BQ.1, BQ.1.1, XBB and XBB.1 variants pathogenicity is identical as in the Omicron BA.2.75 variant. On the other hand, infectivity of the Omicron BQ.1, BQ.1.1, XBB and XBB.1 variants is greater than those of previous variants. This will most likely result in the phenomenon of asymmetric coinfection, that is circulation of several variants in the population, some being dominant.

This article identifies promising research areas on the PETROLEUM SCIENCE topic via bibliometric analysis of the 2018-2021 publications in the highly cited Journal of Petroleum Science and Engineering, which is included in the Journal Citation Reports Section: ENERGY & FUELS — Q2 Quartile, ENGINEERING, PETROLEUM — Q1 Quartile. Bibliometric metadata from Web of Science were used for 866 articles in 2018, 1,142 — in 2019, 1,138 — in 2020, and 1,832 in 2021. The clustering of articles was performed using the texts of the Title, Abstract, Keywords, and Keywords Plus fields. The demo version of the Lingo3G algorithm was used. For the two major clusters, the most promising research topics were determined by comparing the titles of the 350 most cited and 350 least cited articles for each year. The hypothesis that low-cited papers often have the same subject matter as high-cited papers of previous years is discussed.

An exact solution is obtained for the kinetic energy in the general case of the spatial motion of solids with arbitrary rotation, which differs from the Koenig formula by three additional terms with centrifugal moments of inertia. The description of motion in the Lagrange form and the superposition principle are used, which provides a geometric summation of the velocities and accelerations of the joint motions in the Lagrange form for any particle at any time. The integrand function in the equation for kinetic energy is represented by the sum of the identical velocity components of the joint plane-parallel motions. The moments of inertia in the Koenig formula do not change during movement and can be calculated from the current or initial state of the body. The centrifugal moments change and turn to 0 when rotating relative to the main central axes only for bodies with equal main moments of inertia, for example, for a ball. In other cases, the difference in the main moments of inertia leads to cyclic changes in the kinetic energy with the possible manifestation of precession and nutation, the amplitude of which depends on the angular velocities of rotation of the body. An example of using equations for a robot with one helical and two rotational kinematic pairs is given.

This article examines illicit financial flows (IFFs) perpetuated in financial statements to develop a framework to curb IFFs. IFFs create opacity, impeding economic progress through investment deterrents and financial uncertainty. Through a comprehensive literature review and the synthesis of sets of qualitative propositions, the researchers previously developed a conceptual framework to address IFFs, and the purpose of the present article is to strengthen and validate the framework among stakeholders in the financial and audit sectors. Following a mixed inductive and deductive research approach and a qualitative methodological choice, the researchers conducted interviews among practitioners to enhance the framework, followed by a focus group to validate the framework. IFF challenges that emerged are tax evasion, for example, investments in untraceable offshore accounts, harming the economy, bitcoins not being subject to regulation everywhere in the world and being used by cryptocurrency criminals to transfer IFFs to nations with lax regulations. Internationally, IFF risks are also determined by geographical position, trade links and porous borders among countries emerged as further challenges, calling for entities to execute existing policies, improve tax enforcement methods, apply cross-border coordination, and practice financial reporting transparency aimed at combatting IFF practices. On the strength of these, the industry surveys significantly enhanced the conceptual framework.

The existence of semantic inaccuracy §2 articles by A. Einstein “On the electrodynamics of moving bodies”, illustrating the principle of the relativity of simultaneity. In a sentence “The observers co-moving with the moving rod would thus find that the two clocks do not run synchronously while the observers in the system at rest would declare them synchronous”, the preposition “not” must be moved and placed before the word “synchronous”, at the end of the sentence. The criterion for synchronism of the clocks on the rod is the time it takes for the beam to travel from clock A to clock B and back, and for observers on the rod it will be the same both when the rod is at rest and when the rod is moving. Otherwise, observers on the rod would have had at their disposal an experiment that would allow them to determine whether the rod is moving or at rest, which contradicts the first postulate of the Special Theory of Relativity. The inaccuracy is found unchanged in modern editions in English and Russian, and can become a source of difficulties when teaching the basics of the Special Theory of Relativity based on primary sources, as well as make it difficult to understand the author’s logic when studying independently. Nobel laureate R. Feynman, having expanded and modified A. Einstein's thought experiment, eliminated the inaccuracy described in the article.

The prim objective of commenced article is to determine q-sumudu transforms of a product of unified family of q-polynomials with basic (or q-) analogue of fox’s H-function and q-analog of I-functions. Specialized cases of the leading outcome are further evaluated as q-sumudu transform of general class of q-polynomials and q-sumudu transforms of the basic analogues of Fox’s H-function and I-functions.

In this research paper, we implement the theory of the primorial function, to develop the Supremum and Infimum bounds for the sum of (log(p))². There are, however, considerable computational difficulties related to these bounds. Therefore, from a pragmatic point of view, a set of Upper and Lower bounds had been developed to bypass this issue. Despite the increased estimation error, the Upper and Lower bounds are still considered sufficiently accurate, while facilitating an easy and fast computation of the estimate of the sum.

The difficult current environmental situation, caused by construction industry residues containing ceramic materials could be improved by using these materials as recycled aggregates in mortars, with their processing causing a reduction in their use in landfill, contributing to recycling and also minimizing the consumption of virgin materials. Although some research is currently being carried out into recycled mortars, little is known about their stress-strain (σ-ε); therefore this work will provide the experimental results obtained from recycled mortars with recycled ceramic aggregates (with contents of 0, 10, 20, 30, 50 and 100%), such as: the density, the compression strength, as well as the σ-ε curves representative of their behavior. The values obtained from the analysis process of the results are those of: σ (elastic ranges and failure maximum), ε (elastic ranges and failure maximum), and Resilience and Toughness; in order to finally obtain, through numerical analysis, the equations to predict their behavior (related to their recycled content). At the end of the investigation it is established that mortars with recycled ceramic aggregate contents of up to 20% could be assimilated just like mortars with the usual aggregates, and the prediction equations produced could be used in cases of similar applications.

The center of gravity of a three-dimensional object found through an experimental method can be made easier and faster than when calculating the movement manually in order to make the movement in computer graphic images look more natural. In addition, in various sports such as skating, the score can be increased by appropriately moving the position of the center of gravity. Lastly, it is expected that it can be used even when the performance is high in the manufacturing process to increase the stability and speed of various means of transportation (eg, automobiles, airplanes, etc.).

Every subset S of a Cartesian spaces Rd, endowed with differen- tial structure C∞(S) generated by restrictions to S of functions in C∞(Rd), has a canonical partition M(S) by manifolds, which are or- bits of the family X(S) of all derivations of C∞(S) that generate local one-parameter groups of local diffeomorphisms of S. This partition satisfies the frontier condition, Whitney’s conditions A and B. If M(S) is locally finite, then it satisfies all definitions of stratification of S. This result extends to Hausdorff locally Euclidean differential spaces.

Modulation of nociception allows animals to prioritise their survival by adapting their behaviour in different contexts. In mammals, this is executed by neurons from the brain, and is referred to as the descending control of nociception. Whether insects have this control, or have the neural circuits underpinning it, has not been clarified. Here, we review evidence supporting descending control of nociception control in insects, and consider which neuronal sub-types and brain areas may be involved.

The Copenhagen interpretation of quantum mechanics and the notion of superposition of states are examined and found problematical.

The great Greek philosopher Aristotle (384–322 BCE) is almost unanimously acclaimed as the founder of zoology. There is a consensus that he was interested in attributes of animals, but whether or not he tried to develop a zoological taxonomy remains controversial. Fürst von Lieven and Humar compiled a data matrix and showed, through a parsimony analysis published in 2008, that these data produced a hierarchy that matched several taxa recognized by Aristotle. However, their analysis leaves some questions unanswered because random data can sometimes yield fairly resolved trees. In this study, we update the scores of many cells and add four new characters to the data matrix (147 taxa scored for 161 characters) and quote passages from Aristotle’s Historia animalium to justify these changes. We confirm the presence of a phylogenetic signal in these data through a test using skewness in length distribution of a million random trees, which shows that many of the characters discussed by Aristotle were systematically relevant. Our parsimony analyses on the updated matrix recover far more trees than reported by Fürst von Lieven and Humar, but their consensus includes many taxa that Aristotle recognized and apparently named for the first time, such as selachē (selachians) and dithyra (Bivalvia). This study suggests that even though taxonomy was clearly not Aristotle’s chief interest in Historia animalium, it was probably among his secondary interests. These results may pave the way for further taxonomic studies in Aristotle’s zoological writings in general. Despite being almost peripheral to Aristotle’s writings, his taxonomic contributions are clearly major achievements.

Introductions- Liveable housing environments face the menace of global climate change. In-frastructure (including buildings and houses) continuously experiences significant impacts ex-acerbated by natural variability in climate. The study addressed how climate change impacts the resilience of residential buildings, increased maintenance frequency, and the wellbeing and comfort of residents in UK residential buildings. Design/Methodology/Approach: This study used deductive reasoning and an empirical epis-temological methodology as a data-collecting basis to evaluate primary information obtained via a questionnaire. Regression analysis was ultimately employed to analyse the collected da-ta. Result/Discussions: The findings show a significant relationship between climate change and the wellbeing of the occupants of UK residential buildings. Also, the results show that social wellbeing is more important to the occupants than mental and physical wellbeing. It reveals that residential buildings in the UK suffer the cost of maintenance due to continuous reduc-tion in the building fabric's resilience to the impacts of climate change; for instance, a recent increase in rainfall/storms results in unprecedented flooding, which continues to damage the UK residential building fabrics.

This paper intends to examine whether a company’s policy of dividend would impact or influence the price fluctuation of respective common stock in the designated stock market particularly in Hong Kong. Hence, there are 354 companies in Hong Kong which are all listed company on the Stock Exchange of Hong Kong (HKEX), were chosen as sample data for analysis with their audited financial information ranged from 2001 to 2020. Two proxies, the yield and the ratio of pay-out of dividend were widely adopted for the measurement of the effect of dividend policy and the possible influence on the volatility of respective common stock price by employing two statistical models, which are the model of fixed and random effects to provide accurate regression coefficients. With the results from the model of fixed effects, the yield and the pay-out ratio of dividend have been found statistically associated to the price volatilization of respective common stock in Hong Kong. The statical results in this study also find the yield and the ratio of pay-out of dividend are apparently and negatively associated to the price volatilization of respective common stock in Hong Kong. The research outcomes will be a reference for further study on different variables may have effects or volatilize the price of common stock price particularly in Hong Kong capital market.

The imperfection of theoretical and methodological approaches to regulate the jump process transition when combining differentiated energy resources is a pressing issue. The goal of this paper is to develop a theory and a method to regulate the integration-balancing processes of combining diversified resources. The concept of combining integration and balancing models has been substantiated by methods of transforming multidimensional space and approximating generalized functions that represent jump-like processes. Theoretical and operational-regulatory models of economic sustainability have been developed, substantiating new concepts, patterns, properties, dependencies and indicators of the dynamics of the processes of combination; the optimality conditions for the number of approximations of generalized functions, interpreting the effects of control functions of combining resources, are determined. New methods for solving problems have been developed: the organization of the energy technology complex of facilities for enhanced resource diversification and the Center for Sustainability, improving the quality of managing dynamic processes in terms of combining and diversifying resources.

Purpose of this article is to introduce a modification of Phillips operators on the interval $\left[ \frac{1}{2},\infty \right) $ via Dunkl generalization. This type of modification enables a better error estimation on the interval $\left[ \frac{1}{2},\infty \right) $ rather than the classical Dunkl Phillips operators on $\left[ 0,\infty \right) $. We discuss the convergence results and obtain the degrees of approximations. Furthermore, we calculate the rate of convergence by means of modulus of continuity, Lipschitz type maximal functions, Peetre's $K$-functional and second order modulus of continuity.

Purpose of this article is to introduce a modification of Phillips operators on the interval $\left[ \frac{1}{2}% ,\infty \right) $ via Dunkl generalization. This type of modification enables a better error estimation on the interval $\left[ \frac{1}{2},\infty \right) $ rather than the classical Dunkl Phillips operators on $\left[ 0,\infty \right) $. We discuss the convergence results and obtain the degrees of approximations. Furthermore, we calculate the rate of convergence by means of modulus of continuity, Lipschitz type maximal functions, Peetre's $K$-functional and second order modulus of continuity.

In this paper, we study sums of finite products of Chebyshev polynomials of the first kind and Lucas polynomials and represent each of them in terms of Chebyshev polynomials of all kinds. Here the coefficients involve terminating hypergeometric functions ₂F₁ and these representations are obtained by explicit computations.

The biology of sexual orientations has intrigued people for generations. Many models have been providing insights to that topic, but there are still unanswered questions. In humans, sexual orientation has a learned component. Humans have to learn cues by which they identify the sex of their mates, and cues of the emotional messages that those mates broadcast. Many of those cues depend on arbitrary societal conventions. The cues are learned automatically and subconsciously during childhood, based on non-sexual experiences. When sexual orientation emerges at puberty, the youngsters cannot tell how and when they have acquired it. A model that deals with those phenomena is presented. A basic tenet of the model is that a sexual orientation is determined by the innate wirings of the brain. The model describes how the brain learns cues for identifying the sex of the mate, and cues for identifying emotional messages that the mate broadcasts. The learning mechanism is conditioning. The unconditioned stimulus is human voice. The unconditioned responses are the triggers of the physical and emotional manifestations of sexual activity. The model suggests that innate connections from auditory detectors of men’s and women’s voice onto brain centers that trigger sexual activities, such as the hypothalamus, determine the sexual orientation that emerges at puberty. Innate connections from those auditory centers to emotional centers, such as the amygdala, determine the learned emotional cues. It is also proposed that during evolution, the roles of the chemosensory system in identifying mates were taken over by the auditory system.

A simple line of reasoning, based on the most fundamental concepts of thermodynamics, yields some intriguing results for a better understanding of the processes occurring in the observable Universe. Gravitational mass must be continuously generated within an expanding thermodynamic system for this system to remain closed. The Second Law is a direct consequence of this production of mass. Simple expressions for the entropy and temperature of the Universe were obtained and the results agree well with observable values. Furthermore, it is demonstrated that the conservation laws within the Universe are independent of its energy density. Based on the solution for the quantum state of the Universe, it is conjectured that the Second Law is incomplete and must be complemented to a conservation law, which takes into account the growth of the amount of information within the Universe. Once the Second Law is complemented to a conservation law, the importance of mass generation within the Universe becomes well pronounced – not only gravitational effects play the role of an organising force, but also the amount of mass within the Universe defines both the amount of information within the Universe and the level of the Universe’s complexity.

Qualtra, an innovative 10 MW geothermal power plant proposal, employs a closed-loop design to mitigate emissions, ensuring no direct release into the atmosphere. A thorough assessment utilizing energy and exergy analysis, Life Cycle Assessment (LCA), Exergo-economic and Exergo Environmental Analysis (EevA) was conducted. The LCA results, utilizing the ReCiPe 2016 midpoint methodology, encompass all the spectrum of environmental indicators provided. The technology implemented makes it possible to totally avoid direct atmospheric emissions from the Qualtra plant, so the environmental impact is mainly due to indirect emissions over the life cycle. The results obtained for the Global Warming Potential indicator is about 6.6 g CO2/kWh, notably lower compared to other conventional systems. Contribution analysis reveals that the construction phase dominates, accounting for over 90% of the impact in all LCA MidPoint categories. At the component level, a single score calculation incorporating normalization and weighting was executed. The resulting single score is then used into Exergo-Environmental Analysis (EEvA), highlighting the well system as the most impactful contributor, constituting approximately 45% of the total impact. Other substantial contributions to the environmental impact include the condenser (21%), the turbine (17%), and the HEGeo (14%). The Exergo-Economic analysis assesses cost distribution across major plant components, projecting an electricity cost of about 8.3 c€/kWh.

With the proliferation of electric vehicles (EVs) and the consequential increase in EV battery circulation, the need for accurate assessment of battery health and remaining useful life (RUL) is paramount, driven by environmentally friendly and sustainability goals. This study addresses this pressing concern by employing data-driven methods, specifically harnessing deep learning techniques to enhance RUL estimation for lithium-ion batteries (LIB). Leveraging the Toyota Research Institute Dataset, consisting of 124 lithium-ion batteries cycled to failure and encompassing key metrics such as capacity, temperature, resistance, and discharge time, our analysis substantially improves RUL prediction accuracy. Notably, the Convolutional-LSTM-Deep Neural Network (CLDNN) model and the Transformer-LSTM (Temporal-Transformer) model have emerged as standout Remaining Useful Life (RUL) predictors. The CLDNN model, in particular, achieved a remarkable Mean Absolute Error (MAE) of 84.012 and a Mean Absolute Percentage Error (MAPE) of 25.676. Similarly, the Temporal-Transformer model exhibited notable performance with an MAE of 85.134 and a MAPE of 28.7932. These impressive results were achieved through the application of Bayesian hyperparameter optimization, further enhancing the accuracy of predictive methods. These models were benchmarked against existing approaches, demonstrating superior results with an improvement in MAPE ranging from 4.01% to 7.12%.

This paper utilizes a life cycle assessment to evaluate the energy resources and environmental impacts of three heating systems. In the first step, life cycle assessments (LCAs) are prepared for the renewable, conventional, and combined systems. The system boundary of the LCA method is "cradle to gate," which means it includes the entire life cycle of a product or process. This includes energy supply, transport, energy generation, and operational energy use. The system scenarios were compared based on ecological and energy loads in the second step. Primary energy, resources, emissions, and environmental potentials are calculated for the system scenarios using the life cycle assessment methods CML 2016 and ReCiPe 2016. Finally, models for environmental reliability and complex decision-support tools have been developed. This work addresses two main questions: how to characterize the environmental and energy impacts of the systems under study, and what is the optimal scenario for the system? The research results show that the gas boiler system has a greater environmental impact. The most significant differences between the systems relate to the degrada-tion of abiotic fossil fuels when using a gas boiler and the process of acidification when using an electric heat pump. The results can be used to develop sustainable heating systems with reduced environmental impacts and enhanced energy efficiency.

Parasitic diseases are among the most serious public health problems in developing countries, causing high mortality and economic burdens. The Trypanosomatidae family includes Leishmania spp. and Trypanosoma cruzi, which cause leishmaniasis and Chagas disease, respectively. Both these parasites are among the major agents of neglected tropical diseases (NTDs). Leishmaniasis is currently the second most widespread vector-borne parasitic disease after malaria. The World Health Organization (WHO) records approximately 0.7-1 million newly diagnosed leishmaniasis cases each year, resulting in approximately 20,000-30,000 deaths. Also, 25 million people worldwide are at risk of Chagas disease and an estimated 6 million people are infected with Trypanosoma cruzi. Despite the similarity of the parasites, the evolution of the disease and the clinical indexes are explicitly different in African, Asian, and American forms of infection. Clinical therapies are associated with drug resistance and side effects. As a result, the discovery of novel therapeutic agents has emerged as a top priority and a promising alternative. Peptide-based drugs are an attractive class of therapeutic agents. They are recognized for being highly selective and effective, as well as safe and well tolerated. Moreover, they are now being explored in the development of novel therapeutics for a variety of health disorders, including oncology, endocrinology, metabolic, cardiovascular, and bone diseases. In addition, they are being explored in tropical diseases such as leishmaniasis and Chagas disease. We will discuss current knowledge regarding the parasite life cycle, signs and symptoms, diagnosis, and treatment options for leishmaniasis and Chagas disease.

In the past decade, under the background of the carbon peaking and carbon neutrality era, the rapid development of new energy vehicles has put forward higher requirements for the performance of strike forces such as battery cycle life, energy density and cost. Lithium-ion batteries have gradually become the mainstream of electric vehicle power batteries due to their excellent energy density, rate performance and cycle life. At present, the most widely used cathode materials for power batteries are lithium iron phosphate (LFP) and ternary nickel-cobalt-manganese (NCM). However, these materials exhibit the bottlenecks that limit the improvement and promotion of power battery performance. In this review, the performance characteristics, cycle life attenuation mechanism (including structural damage, gas generation and active lithium loss, etc.) and improvement methods (including surface coating and element-doping modification) of LFP and NCM batteries are reviewed. Finally, the development prospects of this field are proposed.

The transportation sector significantly contributes to greenhouse gas emissions, thus imposing sig-nificant environmental hazards. In response, many nations have begun promoting Electric Vehicles (EVs) as an ecologically sustainable substitute for traditional combustion vehicles. Lithium-Ion Batteries (LIBs), characterized by their high energy density, extended lifespan, and relatively low self-discharge rate, have become the suitable energy storage system for EVs, advancing the field of sustainable transportation. The decreasing cost of LIB production has primarily driven this progression, realized through a combination of economies of scale, advancements in electrode materials and cell design, and re-finements in manufacturing processes. All these elements collectively contribute to the cost reduc-tion of LIBs. A foreseeable implication of this trend is an expected surge in LIBs that will exhaust their operational lifespan and, consequently, be cast away into the environment. One mitigating strategy for this impending environmental issue involves collecting these spent EV batteries, re-configuring them, and repurposing them for applications with less rigorous weight, performance, and size prerequisites. This approach would not only prolong the utility life of the batteries but also ameliorate their environmental impacts. Nevertheless, second-life batteries are anticipated to exhibit a higher rate of gas evolution, necessitating comprehensive testing and readjustments to ensure safe operation in secondary ap-plications. The regulatory framework encompassing the second-life battery sector still needs to be defined regarding norms, technical standards, and legislation. This paper aims to elucidate the primary regulations and technical standards proposed thus far in the second-life battery marketplace. The results underscore the exigency for neutral evaluative entities, such as universities, research centers, and governmental institutions, to perform an unbi-ased assessment of the second-life battery market. This would pave the way for novel technical standards and legislative measures specifically tailored to this emerging sector.

Elucidating physicochemical processes in the degradation of pollutants may optimize their removal from water sources. This work presents a set of elementary steps in the photocatalytic degradation of carbamazepine (CBZ), assuming a steady state approximation in an Advanced Oxidation Process (AOP) combining short-wave ultraviolet radiation (UVC), homogeneous reagent (H₂O₂) and heterogeneous (TiO₂) catalyst. Elementary steps include excitation of both reagent/catalysts by UVC photons, adsorption of CBZ on the excited TiO₂, or its oxidation by hydroxyl radicals. Assuming the steady state approximation on the intermediate products (excited TiO₂, CBZ- excited TiO₂ complex, and hydroxyl radicals), leads to rate laws for degradation of CBZ, in which UVC radiation, TiO₂, and H₂O₂ are pseudo first order at all concentrations or intensities having no direct influence on CBZ pseudo-order, whereas CBZ shifts from pseudo first order at low concentrations to pseudo-zero order at high concentrations. Several experiments to test the mechanism were conducted, by varying CBZ, H₂O₂, and TiO₂ concentrations, and UVC radiation intensities. Measured results indeed fit the suggested mechanism for the first three, but irradiation intensity appears to shift CBZ influence from pseudo-second to pseudo-first order with increased intensities. Corrected elementary steps are proposed to fit the results.

The evolutionary transition from single-celled to multicellular individuality requires organismal fitness to shift from the cell-level to the cell group. This reorganization of fitness occurs by allocating the two components of fitness, survival and reproduction, between two specialized cell types in the multicellular group – soma and germ, respectively. How does the genetic basis for such fitness reorganization evolve? One possible mechanism is the co-option of life-history genes present in the unicellular ancestors of a multicellular lineage. Single-celled organisms must regulate their investment in survival and reproduction in response to environmental changes, particularly decreasing reproduction to ensure survival when stressed. Such stress response life-history genes can provide the foundation for the genetic basis for cellular differentiation in multicellular lineages. The regA gene family in volvocine green algae provides a model system to study how this co-option occurs. We discuss the origin and evolution of the volvocine regA gene family, including regA – the gene that controls somatic cell development in the model organism Volvox carteri. We hypothesize that the co-option of life history trade-off genes is a general mechanism involved in the transition to multicellular individuality, making volvocine algae and the regA family a useful template for similar investigations in other lineages.

This paper presents PrognosEase; a software that provides an easier way to produce different types of run-to-failure data mimicking real-world conditions to simplify prognosis studies in terms of data collection and improvement in ML degradation modelling process. Different types of degradation types made available to meet different types of applications. Besides, some primary ML tests were performed to ensure that complexity patterns of real systems could be observed in the training/testing predictions attitude. This paper also presents the impacts, limitations and potential improvements of the data generator.

The interactions between work and personal life are important for ensuring well-being especially during COVID-19 where the lines between work and home are blurred. Work-life interference/imbalance can result in work-related burnout, which has been shown to have negative effects on faculty members’ physical and psychological health. Although our understanding of burnout has advanced considerably in recent years, little is known about the effects of burnout on nursing faculty turnover intentions and career satisfaction. Thus, this study aimed to test a hypothesized model examining the effects of work-life inference on nursing faculty burnout (emotional exhaustion and cynicism), turnover intentions and ultimately, career satisfaction. A predictive cross-sectional design was used. An online national survey of nursing faculty members was administered throughout Canada in Summer 2021. Nursing faculty who held full-time or part-time positions in Canadian academic settings were invited via email to participate in the study. Data was collected from an anonymous survey housed on Qualtrics. Descriptive statistics and reliability estimates were computed. The hypothesized model was tested using structural equation modeling. Data suggest that work-life interference significantly increase burnout which contribute to both higher turnover intentions and lower career satisfaction. Turnover intentions in turn was negatively associated with career satisfaction. The findings add to the growing body of literature linking burnout to turnover and dissatisfaction, highlighting key antecedents and/or drivers of burnout among nurse academics. These results provide suggestions for suitable areas for the development of interventions and policies within the organizational structure to reduce the risk of burnout during and post-COVID-19 and improve faculty retention.

Using the PLCC advantages generates revenue and lowers costs more than might be anticipated. It ensures better decisions by using a more perfect and practical assessment of revenues and costs. Although the supplier selection (SC) product life cycle cost (SCPLCC) is an important consideration among corporations, SCPLCC problem has become associated with deciding how one supplier should be selected from possible alternatives. In this study, we applied two types of goal programming, multiobjective linear programming and revised multichoice goal programming to develop a PLCC-based concept to solve the SCPLCC problem and construct a decision-making tool for application to a case of supply chain management in a Taiwanese light-emitting diode company in the high-tech industry. Our study main contribution Company managers can easily use the two approaches of the SCPLCC model with different parameters to solve SCPLCC model problems. Finally, we comparing five models found RMCGP with weighted linear goal programming had an adequate effect for application to the PLCC concept for high-technology company; this cloud make company decision–makers focus on low PLCC and select better supplier.

The inaccurate estimates of linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrades speech enhancement performance. The existing methods proposed a tuning of the biased Kalman gain particularly in stationary noise condition. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then construct a whitening filter (with its coefficients computed from the estimated noise) and employed to the noisy speech, yielding a pre-whitened speech, from where the speech LPC parameters are computed. Then construct KF with the estimated parameters, where the robustness metric offsets the bias in Kalman gain during speech absence to that of the sensitivity metric during speech presence to achieve better noise reduction. Where the noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on NOIZEUS corpus demonstrates that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

China's implementation of public-private partnerships projects has been quite effective, involving infrastructure and other livelihood projects, a total of 19 industries, and an investment of nearly 1.5 trillion yuan. The characteristics of PPP projects such as long construction period and large investment amount determine the risks of PPP projects are also great, and the PPP projects of comprehensive environmental governance are also the same. The government and social capital use the PPP model to cooperate, and use the principal component analysis method to assess the risks of the entire life cycle of the comprehensive environmental governance PPP project. Therefore, it plays an important role in ensuring the smooth implementation of projects and reducing the losses caused by risks. According to the risk factors of the whole life cycle of the comprehensive environmental governance PPP project, an indicator system of 5 first-level indicators, 18 second-level indicators, and 43 third-level indicators has been established. Principal component analysis is used to analyze the influence weight of risk factors at each stage. The analysis shows that among the four stages, environmental pollution risk, project approval delay risk, completion risk, interest rate and financial fluctuation risk, and franchise life risk are the most influential risks in the implementation of PPP projects. Therefore, suggestions are made through the risk factors of each stage in the comprehensive environmental governance PPP project. For example, strengthen the response to the external environment risks of the comprehensive environmental governance PPP project, standardize the bidding and procurement of the comprehensive environmental governance PPP project, and strengthen the subsequent management of the transfer of the comprehensive environmental governance PPP project. In this way, the ability to resist risks of comprehensive environmental governance PPP projects is improved; the smooth implementation of the project is guaranteed, and the long-term development of comprehensive environmental governance PPP projects is promoted.

Tourism is the main economic sector in the Balearic Islands (Spain) and hotel housekeepers (HHs) are a large occupational group, in which stress is becoming a major issue. This study aims at exploring in-depth factors perceived as stressors by HHs and key-informants, and their effects on work-life balance (WLB). A qualitative design with phenomenological approach was used, conducting six focus groups with 34 HHs and 10 individual interviews with key-informants. Results were analyzed adopting the job demands-resources model and a gender perspective. High demands –e.g work overload, time pressure, physical burden...-, lack of enough resources and little control –derived from role conflict, unexpected events...- were the most important factors explaining HHs’ stress. Additionally, this imbalance was perceived as leading to health problems –mainly musculoskeletal disorders-. Working schedule was mentioned as a facilitator to WLB, whereas an imbalance between job demands and resources led to work-home conflict –preventing them from enjoying leisure time-. Multiple roles at work and at home increased their stress. HHs experienced their job as invisible and unrecognised. Regarding practical implications, our recommendations for hotel organization include reducing workload and increasing resources, which would improve the job demands-resource balance, diminish negative mental and physical outcomes and improve WLB.

Since the beginning, humans advanced their civilization by making better tools to improve their lives. Tools and products were designed for better living considering manufacturing issues, cost and time as predominant criteria. It has become clear that not considering environment and society, both at local/global levels, has now become a major impediment affecting living conditions on a large portion of the Earth and in many societies. Design methodologies should lead to creative solutions with consideration to engineering and economics for practicality but also to environmental and social constraints for sustainability. We propose a comprehensive design methodology based on multidisciplinary design to include the knowledge of humanities, environmentalists, science and engineering, and allowing for experts’ inputs from these areas to provide a holistic approach to engineering design . For example, experts in humanities are expected to interact with stakeholders to evaluate their value systems to provide guidance for the design. The methodology that we synthesize is new and combines (i) Societal level impacts at all scales, (ii) Environmental impacts and (iii) Engineering design with economic impacts, including uncertainty considerations. The proposed design methodology is called Social-Environmental-Economical-Engineering Framework (SEEEF). It can utilize concepts and tools such as Circular Design, Doughnut Economics, design based on environmental life cycle analysis, among others. SEEEF is quantity based and provides steps for evaluating any project or product in an objective manner and will help train engineers in design for sustainability. It also provides non-engineers with a significant role in design to increase their understanding of the hard constraints of engineering. Ultimately, SEEEF allows society to take an informed decision considering short/long term and local/global impacts of the design and the pertinent uncertainties.

In financial time series modelling and forecasting, combining ARMA and GARCH models tend to produce superior and reliable models for volatility persistence, half-life volatility and backtesting (application of model in real life). In Nigeria, banking stocks are mostly traded because of its potential benefits to investors. This study modelled and forecasted the Guaranty Trust (GT) Bank daily stock returns from January 2^nd 2001 to May 8^th 2017 data set collected from a secondary source. The ARMA-GARCH models, persistence, half-life and backtesting were used to analysed the collected data using student t and skewed student t distributions, and the analyses are carried out R environment using rugard and performanceAnaytics Packages. The study revealed that using the lowest information criteria values only could be misleading rather we added the use of backtesing. The ARMA(1,1)-GARCH(1,1) models fitted exhibited high persistency in the daily stock returns while the days it takes for mean-reverting of the models ranges from 5 days to 100 days but unfortunately the models failed backtesting. The results further revealed ARMA(1,1)-eGARCH (2,2) model with student t distribution provides a suitable model for evaluating the GT bank stock returns among the competing models while it takes less than 30 days for the persistence volatility to return back to its average value of the stock returns. This study recommended that researchers should adopt backtesting approach while fitting GARCH models while GT bank stocks investor should be assured that no matter the fluctuations in the stock market, the GT bank stock returns has the ability to returns to its mean price return.

A tailored model for the assessment of environmental benefits achievable by “light-weighting” in the automotive field is presented. The model is based on the Fuel Reduction Value (FRV) coefficient, which expresses the Fuel Consumption (FC) saving involved by a 100 kg mass reduction. The work is composed of two main sections: simulation and environmental modelling. Simulation modelling performs an in-depth calculation of weight-induced FC whose outcome is the FRV evaluated for a wide range of Diesel Turbocharged (DT) vehicle case studies. Environmental modelling converts fuel saving to impact reduction basing on the FRVs obtained by simulations. Results show that for the considered case studies, FRV is within the range 0.115–0.143 and 0.142–0.388 L/100 km × 100 kg, respectively, for mass reduction only and powertrain adaptation (secondary effects). The implementation of FRVs within the environmental modelling represents the added value of the research and makes the model a valuable tool for application to real case studies of automotive lightweight LCA.

Stormwater runoff from large roads is a major source of pollutants to receiving waters. Porous pavements have been shown to substantially reduce many of these pollutants, but studies are lacking on arterial roads. We sampled typical stormwater pollutants in runoff from sections of an arterial road 9-16 years after installation of three pavement types: control with conventional asphalt, porous asphalt overly, and full-depth porous asphalt. Both types of porous pavements substantially reduced most of the stormwater pollutants measured. Total suspended solids, turbidity, total lead, total copper, and 6PPD-quinone were all reduced by >75%. Total nitrogen, ammonia, total phosphorus, biochemical oxygen demand, total and dissolved copper, total mercury, total zinc, total polycyclic aromatic hydrocarbons, and di-2-ethylhexyl phthalate were all reduced by >50%. Reductions were lower or absent for nitrate, orthophosphate, E. coli, dissolved lead, and dissolved zinc. Most reductions were statistically significant. Many pollutants exceeded applicable water quality standards in the control samples but met them with both types of porous pavement. This study demonstrates that porous overlays and full-depth porous asphalt can provide substantial reductions of several priority stormwater pollutants on arterial roads for many years after installation.

This study investigates the characteristics of formaldehyde (HCHO) concentration and its reduction in newly constructed apartment buildings in Dubai. It addresses the significant health risks of high HCHO levels due to poor ventilation and extensive use of VOC-emitting materials. The research explores the effectiveness of various mitigation strategies, including air purifiers, photocatalyst applications, and window ventilation, in the living room, master bedroom (MBR), Room 1, and Room 2. HCHO concentrations were measured under different conditions: sealed windows, operation of an air purifier with a VOCs filter, and the application of a photocatalyst. The study followed a quantitative approach, recording real-time HCHO levels over 24 hours. Results indicated that sealing windows led to HCHO concentrations surpassing the domestic standard of 0.1 ppm, with the MBR recording the highest concentration at 0.73 ppm. The air purifier showed minimal impact within the first 8 hours but achieved an 18% reduction after 24 hours of operation. Applying a photocatalyst to room surfaces resulted in a 50% reduction in HCHO levels. Ventilation through window openings significantly improved air exchange rates, with the air exchange per hour (ACH) increasing proportionally with window opening size. Smaller rooms with larger window-to-volume ratios, such as Room 1 and Room 2, maintained HCHO concentrations below the WHO standard of 0.1 ppm with partial window openings. The study concludes that window ventilation is the most effective strategy for reducing HCHO concentrations, highlighting the importance of integrating comprehensive ventilation designs in modern residential buildings to ensure healthier indoor environments in Dubai.

Under the wave of technological advancement and industrial upgrading, new productivity is deeply influencing the field of vocational education and promoting fundamental changes in teachers' roles. Modern teachers must not only keep pace with technological development but also master innovative teaching modes and guide students to form correct values. They are gradually evolving from mere knowledge transmitters to guides for learning, stimulators of interest, explorers of in-depth research, and promoters of comprehensive growth. Through interdisciplinary knowledge structures and innovative abilities, combined with advanced tools such as information technology and artificial intelligence, teachers innovate teaching methods to meet students' diverse learning needs and focus on cultivating students' professional skills, comprehensive qualities, and innovative spirits. This multi-dimensional reshaping of roles is not only an inevitable choice for the development of vocational education but also provides important insights for educational reform.

The aging ability of dry white wines has been increasingly recognised. The present work aimed to understand which sensory factors drive their quality evaluation by experienced tasters. Individuals assessed several aesthetic, synthetic, and analytic flavour attributes, using dark tasting glasses. Wines originated from the grape varieties Alvarinho, Arinto, Sauvignon Blanc and Pinot Bianco with a wide range of ages. Basic physical-chemical analysis, browning (Abs 420 nm), elemental composition and a partial volatile fraction were also determined. The overall quality scores were a function of complexity and balance and were negatively influenced by the perception of faultiness. The different ages could be associated with a continuous sensory space characterised by a decreasing perception of freshness and an increase in the mellowed flavours. All wines shared an austere in-mouth perception elicited by their acidity, saltiness, bitterness, smoothness and dryness. Nevertheless, quality scores were similar from the youngest to the oldest-tasted wines (17 years old). The exception was a Sauvignon Blanc wine from a recent vintage that was judged as faulty due to the perception of earthiness. Overall, the tasted wines displayed an unexpected aging ability as demonstrated by the difference between the predicted and real wine ages. To improve the recognition of old wines, the metaphor “mellowed by age” is proposed to describe flavours resulting from beneficial aging instead of “oxidised by age”.

In the digital age, 3D as-built monitoring of structures and buildings is becoming increasingly important. It provides the basis for automated quality control and progress monitoring through as-built and as-planned model- comparisons, as well as 3D modelling for automated repair, refurbishment and reuse in existing construction. Especially for 3D model-based manufacturing processes such as additive manufacturing, efficient as-built monitoring is essen- tial to ensure the quality and accuracy of buildings and products. Therefore, three different 3D scanning devices have been evaluated for 3D as- built monitoring of concrete structures with different characteristics: An indus- trial handheld laser scanner commonly used in automotive and mechanical en- gineering, a mobile device with lidar for commercial public use, and a terrestri- al laser scanner commonly used for surveying. The devices were tested on 3D printed, cast, and assembled structures of different sizes and geometries. The scan results are compared to the as-planned models based on their dimen- sional distances using Cloud-to-Cloud Comparison (C2C). The results show that the applicability is highly dependent on the size, geometric complexity and surface texture of the concrete structures. While small and flat structures can be captured completely and with micrometer accuracy using the handheld industri- al laser scanner, terrestrial laser scanners are suitable for capturing large struc- tures and environments. The handheld, photogrammetry-based mobile device has demonstrated the broadest applicability for concrete structures. The accura- cy and coverage of the point cloud results were respectively high. Therefore, photogrammetry-based scanning with advanced mobile devices is a cost- effective, fast and portable alternative for 3D as-built monitoring and data ac- quisition of a wide range of concrete structures.

Conclusions: The quantitative method provides a large-scale overview of the biometry measurements of an entire practice. Image audits can then be focused on sonographers whose measurements are outliers, avoiding the time, complexity, and expense of auditing those whose measurements fall within the expected range. We encourage commercial software developers to include tools in their ultrasound reporting software to facilitate such quantitative review.

Introduction: Agricultural practices significantly influence soil microbial populations and physicochemical properties, which are crucial for crop growth and quality. This study aims to investigate the impact of different agrochemical applications on soil microbial dynamics, soil physicochemical properties, as well as yield and proximate properties of maize. Methods: Carefully gathered topsoil samples at depths ranging from 1 to 15 cm, were transported to Jimma University to cultivate maize. Over a period of up to 120 days, soil and maize samples were collected at specified days to analyze various parameters, including soil pH, microbial populations, as well as nutrient content both in soil and plants. The collected data was statistically analyzed using one-way ANOVA, with a significance level of p < 0.05. Results: The soil bacterial and fungal populations were measured on days 5, 10, 20, 40, 80, and 120. The highest total mesophilic bacterial count (TMBC) was measured in the pots containing compost (G) and the lowest in pots that received macronutrient fertilizers and glyphosates (B) (i.e. 91.8 x 105 and 13.13 x 105 cfu/g of soil, respectively). The highest total mesophilic fungal count (TMFC) was observed in pots containing glyphosates and compost (F) (i.e. 67.25 x 104 cfu/g soil) and again the lowest in pot treated with macronutrient fertilizers and glyphosate (B) (i.e. 3.23 x 104 cfu/g soil). Moreover, the pots treated with macronutrient fertilizers and glyphosate (B), macronutrient fertilizers (A), and micronutrient fertilizers (C) exhibited the lowest levels of Fe and Zn. Furthermore, the pots receiving macronutrient fertilizer combined with glyphosate (B), as well as those receiving macronutrient fertilizers (A) alone had the lowest concentrations of Mn and Cu micronutrients. Finally, in maize the lowest protein, fats, and carbohydrates (g/100g) were found in the pots treated with macronutrient fertilizer combined with glyphosate (B, 2.21 ± 0.2), micronutrient fertilizer (C, 8.57 ± 0.25), and glyphosates only (D, 57.34 ± 0.1). Maize treated with compost (G) showed the highest levels of Fe, Cu, and Zn, while macronutrient fertilizer combined with glyphosate (B) resulted in the lowest content of these micronutrients. Additionally, maize receiving micronutrient fertilizer (C) had the highest concentration of Mn, whereas those treated with glyphosates (D) had the lowest. Conclusions: Significant variations in soil’s mesophilic bacterial and fungal populations, micronutrient levels, and nutritional composition were observed, indicating treatment-related changes. Generally, treatment with micromineral fertilizer combined with glyphosate (B) seemed to deplete the soil while compost treatment improved. Compost-treated soils exhibited the highest mesophilic bacterial and fungal count, as well as Fe and Zn micronutrient concentrations. The use of agrochemicals also had a negative effect on maize yield quality. The fluctuations in the soil parameters underscore the multifaceted effects of agrochemical treatments.

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback's significant yet underexplored potential in understanding public library patronage across Taiwan's six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with Social Network Analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan's National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience.

Considering the unquestionable role of education in economic development, it has an impact at a macroeconomic level—contributing to enhancing productivity and innovation, reducing poverty and promoting social cohesion—and at a personal level—ensuring the improvement of knowledge, skills and individual capabilities and promoting social values like empowerment and equality. Notably, quality education is recognized as one of the Sustainable Development Goals (SDGs), which, jointly with other behaviors and attitudes, could impact the development of societies in other fields like health and well-being, cultural preservation, environmental sustainability and even peace and stability—all of them also listed as SDGs. However, the capacity, or not, to reach higher levels of compliance with quality in education (SDG 4) varies from country to country, according to the 2023 Sustainable Development Report results. Thus, the present study used a fuzzy set qualitative comparative analysis to assess the conditions for attaining higher levels of quality education based on the different indicators used to measure SDG 4. The analysis will allow comparisons of results among different countries and provide information about the initiatives that could be relevant to increasing quality education worldwide and for policymakers.

Researchers are exploring solutions to meet the growing demand for protein due to the expected increase in global population by 2050. Interest in alternative protein sources like insects has risen, driven by concerns about environmental impact and the need for sustainable food production. This study aimed to develop and evaluate the physicochemical properties of soy protein-based burger enriched with insect protein from Alphitobius diaperinus. Three formulations were developed: a control (B0), and burgers with 5% (B5) and 10% (B10) insect protein - Whole Buffalo Powder (WBP). Results showed that adding insect protein decreased the burger analogue's pH. There was observed a clear trend of increasing total lipids and SFA, and decreasing MUFA and PUFA, as the WBP concentration increases from 0% to 10%. No significant differences with increasing WBP concentration in the protein content of the burger analogue, as well as the cooking yield, were noted. The WBP addition had a notable effect on the color change, especially decrease in brightness (L*). It was shown that as the WBP concentration increased, there were no significant differences in the texture profile of the burger analogues. The formulation with 5% WBP concentration was the most acceptable in sensory analysis.

This study investigates how customers’ technological readiness affects service quality and the adoption of self-service banking by developing an empirical model. The model was proposed to explore the relationships between technology readiness, self-service quality, and customer satisfaction. The hypotheses and stability of the model were tested using structural equation modeling. The findings revealed that technology readiness, including the optimism, innovation, and insecurity dimensions, had a significant positive impact on self-service quality. In addition, service quality fully mediated the relationship between technological readiness and customer satisfaction. These results suggest that when customers are ready to embrace and use new technology, their evaluation of service quality is high. Thus, bank managers should drive customer perceptions of the quality of self-service banking by improving their technological readiness through various measures. Furthermore, based on the mediating effect of self-service quality, banks must be aware that improving service quality can enhance customer satisfaction and maintain a competitive advantage.

Amylose content (AC) stands as a pivotal determinant of rice grain quality, primarily governed by the Waxy gene (Wx), with its allelic diversity significantly influencing AC variation. In the current culinary landscape, soft rice varieties, known for their low AC and superior palatability, are particularly sought after by consumers. Notably, many soft japonica rice varieties cultivated in southern China, including the Nangeng series, are characterized by the presence of the Wxmp allele, derived from the ancestral Wxmq allele. While both alleles are associated with reduced AC, there remains a paucity of detailed understanding regarding the interplay between specific functional single nucleotide polymorphisms (SNPs) within these alleles and the overarching rice grain quality. Employing site-directed mutagenesis, we engineered three distinct transgenic rice lines, each harboring either the Wxmp, Wxmq, or Wxb-5c alleles, against the background of the glutinous rice cultivar Nip(wx). This suite of transgenic rice showcased a gradation ingrain transparency inversely correlated to AC, delineating a sequence from Wxmq, signifying the lowest, through Wxmp, to Wxb-5c, indicating the highest. Consequently, this gradation in AC precipitated variations in other rice grain physicochemical attributes, such as the taste value of cooked rice, gel consistency (GC), and starch pasting properties. Furthermore, analyses of gene expression and enzyme activity revealed that the functional SNPs, Ex4-53G/A and Ex5-53T/C, lead to a decline in the GBSSI enzyme activity without affecting expression levels. These findings underscore the potential of harnessing these SNPs in breeding programs aimed at developing soft rice varieties endowed with enhanced culinary qualities.

Contamination in coastal regions attributed to fluoride and nitrate cannot be disregarded, given the substantial environmental and public health issues they present worldwide. Maintaining water quality is crucial for environmental well-being. This comprehensive study was performed to assess the spatial as well as indexical water quality, identify contamination sources, and evaluate associated health risks pertaining to nitrate and flouride in AlHassa region, KSA. The physiochemical results revealed a pervasive pollution of the overall groundwater. The dominant water type was Na-Cl, indicating saltwater intrusion and reverse ion exchange impact. Spatiotemporal variations in physicochemical properties suggest diverse hydrochemical mechanisms, with geogenic factors primarily influencing groundwater chemistry. The groundwater pollution index varied between 0.8426 and 4.7172, classifying samples as moderately to very highly polluted. Similarly, the synthetic pollution index (in the range of 0.5021-4.0715) revealed that none of the samples had excellent water quality, with various degrees of pollution categories. Nitrate Health Quotient (HQ) values indicated chronic human health risks ranging from low to severe, with newborns being the most vulnerable. Household use of nitrate-rich groundwater for showering and cleaning did not pose significant health risks. Fluoride HQ decreased with age, and children faced the highest risk of fluorosis. The Hazard Index (HI) yielded moderate to high-risk values. Nitrate risks were 1.21 times higher than fluoride risks, as per average HI assessment. All samples fell into the vulnerable category based on the Total Hazard Index (THI), with 88.89% classified as very high risk. This research provides valuable insights into groundwater quality, guiding water authorities, inhabitants, and researchers in identifying safe water sources and vulnerable human populations. The results highlight the need for appropriate treatment techniques and long-term coastal groundwater management plans.

Abstract Introduction: Poor structural quality of health facilities for tuberculosis (TB) diagnosis and treatment contributes to unfavorable health outcomes among TB patients. Improving the quality of healthcare services helps to avert TB-related morbidity. Despite these facts, the level of structural quality of health facilitieSs to deliver TB services is not in line with what the national TB and leprosy program (NTLP) recommends. Hence, the present study was conducted to assess the structural quality of health facilities to deliver quality TB services to TB patients among public health facilities in the Sidama region.Objectives: To determine the quality of the structural domain of TB services at public health facilities in the Sidama region of southern Ethiopia.Methods: An institution-based cross-sectional study was conducted from June to July 2023 among randomly selected public health facilities in the Sidama region. We included 40 randomly selected public health facilities that delivered TB services in the study area. Data was collected by experienced data collectors using facility audits, clinical observation checklists, structured questionnaires, and in-depth interviews. An Open Kit Mobile (ODK) application was used to collect data and exported to SPSS version 26. All statistical analysis was done using SPSS version 26.Results: The overall mean quality score for the structural dimension was 50.25 among the 40 health facilities audited in the study area.Conclusion: The quality of TB services regarding structural dimensions was poor in the study area. Therefore, any strategies must give careful attention to increasing the comprehensive structural dimension of quality TB services.

In the early 2000s, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) emerged as a performant and relevant tool for identifying micro-organisms. Since then, it has become practically essential for identifying bacteria in microbiological diagnostic laboratories. In the last decade, it was successfully applied for arthropod identification allowing to distinguish vectors from non-vectors of infectious diseases. However, identification failures are not rare, hampering it widely use. Failure is generally attributed either to the absence of respective counter species MS spectra in the database, or to the insufficient quality of query MS spectra (i.e. lower intensity and diversity of MS peaks detected). To avoid matching errors due to non-compliant spectra, the development of a strategy for detecting and excluding outlier MS profiles became compulsory. To this end, we created MSProfileR, an R package leading to a bioinformatics tool through a simple installation, integrating the control quality system of MS spectra and an analysis pipeline including peak detection and MS spectra comparisons. MSProfileR can also add metadata concerning the sample that the spectra are derived from. MSProfileR has been developed in the R environment and offers a user-friendly web interface using the R Shiny framework. It is available on Microsoft Windows as a web browser application by simply navigation using the link of the package on the Github. MSProfileR is therefore accessible to non-computer specialists and is freely available to the scientific community. We evaluated MSProfileR using two datasets including exclusively MS spectra from arthropods. In addition to coherent sample classification, outlier MS spectra were detected in each dataset confirming the value of MSProfileR.

This study investigated the anatomical characteristics and pulp quality of tapped Styrax sumatrana wood to facilitate its further utilization. The transverse surface of the tapped S. sumatrana was light or greyish brown in the sapwood and reddish brown in the heartwood. The resin canals of the tapped wood were formed along the growth rings at the boundaries between the heartwood and sapwood. Furthermore, microscopic analyses revealed an irregular outline and rounded epithelial cells at the edges of the intercellular traumatic resin canal (TC). Approximately 8–16 epithelial cells surrounded the resin canals. The fibers in the tapped S. sumatrana were generally thin-walled, whereas those near the TC were thick-walled. Moreover, S. sumatrana were diffuse-porous and exhibited intermediately distinct or indistinct growth ring boundaries. The vessels were mainly in radial multiples and clusters of 3–5. In addition, they showed a diagonal-to-radial pattern arrangement and a few tangential bands. Deposits were observed in some vessels in the tapped part but were absent in the untapped part. The fiber length of the tapped S. sumatrana wood was classified as moderate to extensive and categorized as 2nd grade pulp quality. Finally, the cell walls were classified as thin to moderate.

This study aimed to use spatiotemporal tensor data to measure the level of employment quality in China's provinces and analyzed the magnitude and direction of its influencing factors in the spatiotemporal dimension. Taked 30 provinces, autonomous regions and municipalities directly under the central government in China from 2011 to 2020 as the research object, the employment quality evaluation system was constructed from six dimensions of employment environment, employment status, employability, labor remuneration, social security, and labor relations. The employment quality index data was expressed as a three-order high-dimensional tensor spatio-temporal data form, and the employment quality of China's provinces was measured from the spatio-temporal perspective by using principal tensor analysis, and the visual analysis of the development and change process of employment quality was carried out. The spatial autocorrelation analysis of employment quality was carried out, and the time-space dual fixed effect model of the spatial Dubin model was selected to analyze the direction and magnitude of the influence factors of employment quality on itself and its neighboring provinces. The research showed that: (1) The overall level of employment quality in China was not high and the employment quality varies greatly among provinces, and the employment quality development gap among provinces showed a trend of widening. (2) The development of employment quality in western China was relatively fast, while the development of employment quality in central China showed insufficient stamina. (3) Sichuan Province had a strong radiation effect on the development of employment quality in neighboring provinces, and Beijing and Tianjin had a strong siphon effect on the development of employment quality in neighboring provinces. (4) The level of industrialization and informatization promoted the development of employment quality in China's provinces, while the industrial structure had a significant negative effect on the development of employment quality.

Origanum dictamnus L. (Lamiaceae), a local endemic plant of Crete (Greece), creates polysaccharide-containing subcuticular compartments presenting biological activity against phytopathogenic fungi, and, among others, significantly affecting the fungal cell wall polysaccharides. This field study introduces a fertilization scheme for O. dictamnus, which was developed and refined to optimize yield as well as critical herbal quality aspects. Five fertilization schemes were investigated, based on a polysaccharide-based Integrated Nutrient Management (INM), a mixture of chemical fertilizers and two biostimulants (not algae) via foliar and soil application. Plant growth, together with leaf chlorophyll fluorescence and colour (SPAD meter, DA meter, Chroma Meter) were determined. The leaf content of chlorophyll, three critical antioxidant compounds (carotenoids, flavonoids, phenols) and minerals were also assessed. Considering all three antioxidants together, the enhanced efficiency, non-toxic, water-soluble, polysaccharide-based INM by foliar application was the most stimulatory scheme, playing an important role in plant growth and development. The present field study provides for the first time baseline fertilization data improving key herbal quality features in O. dictamnus and unravels the attainment of high antioxidant properties. The latter may be exploited in favor of its further utilization as raw material for tea preparation, medicinal purposes, natural food flavoring and/or food preservative.

The pharmaceutical industry is undergoing a shift in processing methods for small-scale production (250-1000 kg/a), leading to an increased focus on continuous production research. Various concepts were developed to overcome challenges in small-scale crystallization for the production of high-quality products. However, further optimization is needed to enhance the industrial applicability of these concepts. This study aims to demonstrate the practical applicability of the Slug Flow Crystallizer (SFC) in the pharmaceutical industry by extending its use to a wider range of substance systems for small-scale API production. Employing an experimental strategy, the research seeks to rapidly assess the SFC's potential for achieving uniform, high-purity product yields, emphasizing the importance of factors such as slug flow stability, residence time distribution, and substance-specific crystallization criteria. This, coupled with our previously published model-based strategy, forms a comprehensive methodology to explore crystallization behavior and modeling for a variety of substance systems within the SFC. The ultimate goal of this study is to showcase the practicality of the SFC in the pharmaceutical industry, enabling the way for its reliable use in long-term, small-scale continuous crystallization and the production of high-quality pharmaceutical products.

The overuse of chemical fertilisers degrades the soil ecosystem and restricts the natural development of plants. Within the coffee industry, various by-products are produced throughout the process of coffee production and consumption, which are significant in terms of environmental waste. SCG (spent coffee grounds) contain a variety of bioactive compounds that have demonstrated potential applications in various fields. These compounds can enhance soil quality by improving its chemical, physicochemical, physical properties, and biological fertility, ultimately leading to improved plant growth. This study examines the impact of SCG, vermicompost, and chemical fertilisers on the growth of organic red radish (Ravanello Cherry Belle) and soil quality. The experiment, conducted in a greenhouse, tested various concentrations and applications of SCG. Results showed that the SCGT 0.5 gm treatment yielded the highest mean plant length (18.47 cm) and fresh weight (27.54 grammes), while the SCG 10% treatment resulted in the lowest mean plant length (4.54 cm) and fresh weight (0.07 grammes). Vermicompost at 50% concentration produced the highest mean leaf surface area (58.32). These findings suggest the potential of SCG as a sustainable fertiliser alternative, contributing to improved plant growth and soil quality, thus supporting sustainable agricultural practices and a circular economy.

In this study, the hybrid biological ion exchange (BIEX) resin and gravity-driven membrane (GDM) process was employed for the treatment of coloured and turbid river water. The primary objective was to investigate the impact of both physical and chemical cleaning methods on ceramic and polymeric membranes in terms of stabilised flux, flux recovery after physical/chemical cleaning, and permeate quality. To address these objectives, two types of MF and UF membranes were utilised (M1 = polymeric MF, M2 = polymeric UF, M3 = ceramic UF, and M4 = lab-made ceramic MF).Throughout extended operation, the resin functioned initially in the primary ion exchange (IEX) region (NOM displacement with pre-charged chloride) and progressed to a secondary IEX stage (NOM displacement with bicarbonate and sulphate), while membrane flux remained stable. Subsequently, physical cleaning involved air/water backwash with two different flows and pressures, and chemical cleaning utilised NaOH at concentrations of 20 and 40 mM, as well as NaOCl at concentrations of 250 and 500 mg Cl2/L. These processes were carried out to assess flux recovery and identify fouling reversibility. The results indicated an endpoint of 1,728 bed volumes (BV) for the primary IEX region, while the secondary IEX continued up to 6,528 BV. At the end of the operation, DOC and UVA254 removal in the effluent of the BIEX columns were 68% and 81%, respectively, compared to influent water. This was followed by 30% and 57% DOC and UVA254 removal by M4 (ceramic MF). The stabilised flux remained approximately 3.8 – 5.2 LMH both before and after the cleaning process, suggesting that membrane materials do not play a pivotal role. The mean stabilised flux of polymeric membranes increased after cleaning, whereas that of ceramics decreased. Enhanced air–water backwash flow and pressure resulted in increased removal of hydraulic reversible fouling, which was identified as the dominant fouling type. Ceramic membranes exhibited higher removal of reversible hydraulic fouling than polymeric membranes. Chemical cleaning had a low impact on flux recovery; therefore, we recommend solely employing physical cleaning.

Multiple sources influence air quality and regional climate in complicated atmospheric emission situations like urban agglomerations. To resolve pollution plumes and source influences in polluted areas, a comprehensive chemical fingerprinting of sources utilizing non-methane hydrocarbons (NMHCs) and the identification of acceptable tracer molecules and emission ratios is required, in contrast to pristine locations, where reliance on a single or a few chemical tracers is frequently sufficient. We have characterized the prevalence of NMHCs fingerprints in the urban areas and quantified the correlation of windspeed with the concentration of these pollutants. eNose sensors and Air quality management stations provide the data to identify the emission sources of such pollutants. Based on our analysis, the average NMHC concentration in 2021 has been recorded 0.424 ppm at point 1 AQMS whereas at point 2 AQMS it was 0.256 ppm. Such outcomes could be attributed to the proximity of emissions sources, the direction and speed of the wind, or both. Moreover, traffic can be a major contributor to pollution levels in any urban area. More research based on a larger dataset is required before definitive conclusions can be drawn, and viable solutions can be proposed.

Ensuring safe, high-quality food is an ongoing priority, yet consumers face heightened risk from foodborne pathogens due to extended supply chains and climate change in food industry. Nanomaterial-based assays are popular and have recently been developed to ensure food safety and high quality. This review discusses strategies for using nanomaterials in colorimetric biosensors. Localized surface plasmon resonance (LSPR) biosensors are commonly utilized for colorimetric biosensing. Several emerging technologies aimed at simple and rapid immunoassays for onsite applications have been introduced in the food industry. In the foreseeable future, field-friendly colorimetric biosensors could be adopted in food monitoring systems. The onsite and real-time detection of possible contaminants and biological substances in food and water is essential to ensure human health safety.

This study presents a comprehensive assessment of indoor particulate matter (PM) concentrations, focusing on PM1, PM2.5, and PM10 in five Primary Health Centers (PHCs): Arakale, Federal College of Agriculture (FECA), Iju, Oba-Ile, and Owode within Akure Local Government Areas in Nigeria. The primary novelty of this research lies in its detailed exploration of the toxicity potential of these PM fractions, providing valuable insights into the local air quality and associated health risks. The study assesses the health implications by calculating the toxicity potential (TP), Relative Risk (RR), Excess Risk (ER), and Attributable Fractions (AF) for cardiopulmonary and lung cancer mortality. The results reveal varying RR values for all-cause mortality, with Arakale showing a slight elevation (RR: 1.061), indicating potential health risks. ER values for cardiopulmonary mortality range from 14.728±7.25 to 19.04±0.38, emphasizing the substantial excess risk associated with long-term PM2.5 exposure. The study also uncovers AF for cardiopulmonary mortality ranging between 11.03±0.31 to 19.22±0.04, underscoring the significant contribution of PM to cardiovascular and respiratory health risks. Similarly, AF for lung cancer mortality ranges from 10.03±0.012 to 17.13±0.10, highlighting the substantial association between PM2.5 exposure and lung cancer risk. These findings underscore the urgent need for targeted air quality management strategies and public health interventions in the studied locations to mitigate the heightened health risks associated with particulate matter pollution. The calculated RR, ER, and AF values offer crucial insights into the complex relationship between PM exposure and adverse health outcomes, providing a foundation for informed decision-making and future research endeavors.

Air quality is one of the most concerning problems in major industrialized cities in the world. Prediction of future air quality is highly relevant to public health. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO2, CO, PM 2.5 and PM 10, over time. At every monitoring time stamp t, we observe one station×feature matrix xt of the pollutant data, which represents a spatio–temporal process. Traditional methods on prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly nonstationary, We propose a de-trending graph convolutional LSTM (long short term memory) to continuously predict the whole station×feature matrix in the next 1 to 48 hours, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transform a nonstationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.

Despite Vitamin B12's recognized importance for the nervous system, there's still a lack of research on the association between Vitamin B12 and sleep, especially in primary care settings. We assessed Vitamin B12 levels in adult primary health care users and investigated correlations with sleep quality, insomnia and sleepiness. In this cross-sectional study, 512 consecutive participants were included. Information regarding anthropometrics, socio-demographics, and medical history was obtained. The Epworth Sleepiness Scale (ESS), Athens Insomnia Scale (AIS), and Pittsburg Sleep Quality Index (PSQI) were used to quantify excessive daytime sleepiness (EDS), insomnia symptoms, and sleep quality, respectively. Median Vitamin B12 was 342 (266, 446) pg/mL. After adjustments, Vitamin B12 levels&lt;342 pg/mL showed significant associations with insomnia symptoms [OR (95% CI) 2.434 (1.331–4.452), p=0.004], especially in elderly, non-obese and female participants, with EDS only in obese participants [OR (95% CI) 3.996, (1.006-15.876), p=0.039]. Nonetheless, there was no significant association between B12 levels and poor sleep quality (OR 1.416, 95% CI 0.678-2.958, p=0.354). In conclusion, our results show that lower Vitamin B12 was associated with insomnia symptoms and sleepiness in specific groups of participants. However, further research with objective measurements of sleep is crucial to assess the relationship between sleep and Vitamin B12.

The menopausal transition often accompanies distressing manifestations, including vasomotor symptoms, sleep disruptions, and depressive syndrome. Omega-3 fatty acids have emerged as a potential intervention to alleviate these symptoms. This review aimed to comprehensively assess the impact of omega-3 supplementation on vasomotor symptoms, sleep quality, and depression among postmenopausal women. We conducted a systematic search focusing on randomized control trials across the Cochrane Library, Web of Science, PubMed, CINAHL, EMBASE, and SCOPUS databases from inception to August 2023. Among the initial pool of 163 studies, 9 studies met the inclusion criteria and were incorporated into this review. Four studies suggested potential benefits of omega-3 intake for improving hot flashes and night sweats. Sleep quality outcomes displayed heterogeneity across the studies. Incorporating diverse scales such as the Hamilton Depression Rating Scale-21, the Patient Health Questionnaire depression scale, and Generalized Anxiety Disorder-7 for depression outcomes, the review found inconclusive evidence on omega-3's impact on depression. Overall, the combined analysis of these studies did not provide substantial evidence to support the efficacy of omega-3 fatty acids in improving vasomotor symptoms, sleep quality, and depression. Further well-designed RCTs with larger participant groups are crucial to validate and generalize these results.

This study takes a two-echelon agricultural products supply chain (APSC) composed of an agricultural product processor (processor) and an agricultural product supermarket (supermarket) as the research object. In order to ensure the stable supply of high-quality agricultural products, this paper considers both fairness concern and corporate social responsibility (CSR), and establishes four Stackelberg game models with or without considering fairness concern and CSR, and obtains the influence mechanism of fairness concern and CSR on bilateral quality efforts in APSC through comparative analysis and numerical analysis. Research shows that the supermarket’s fairness concern can take the role of "profit distribution mechanism", but it is not conducive to improving bilateral quality efforts resulting in the inability to improve market demand, and it is not conducive to improving the utility of the supply chain system and consumer surplus; both members of APSC sharing CSR can not only improve the quality of agricultural products and promote the expansion of market demand, but also can achieve Pareto improvement of both profit of all members and supply chain system. Besides, The CSR shared by both members has a greater impact on bilateral quality efforts decision than the supermarket’s fairness concern, and thus the positive effect of CSR can overcome the negative effect of supermarket’s fairness concern.

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit consists of (i) a sensor and hardware layer that can be configured in a modular manner per research needs, (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study for photo-plethysmography (PPG) sensor with 15 participants, PhysioKit shows strong agreement with research-grade PPG sensors. Furthermore, we report usability survey results from 10 micro-project teams (44 individual members in total) who use PhysioKit for 4-6 weeks, providing insights into its use cases and research benefits. We conclude by discussing extensibility and potential impact of the toolkit on the research community.

Limited scientific evidence shows that alpha lipoic acid (ALA) can induce regression rates of low-grade squamous intraepithelial lesions (LSIL) but the mechanisms of these effects have not been elucidated. To gain a broader insight into its therapeutic potential and mechanisms of action, the effects of 3-month supplementation with 600 mg of ALA on antioxidant- and lipid status param-eters in 100 patients with LSIL were investigated in randomized placebo-controlled study. Obtained results were discussed in terms of patients’ initial metabolic status and diet quality (particularly nutritional intake of antioxidants). Obtained results showed that oxidative status biomarkers were not significantly affected by ALA supplementation. However, serum superoxide dismutase (SOD) activity was positively affected in the subgroup of patients with higher dietary antioxidant intake. Surprisingly, ALA supplementation resulted with small but statistically significant increase of serum low density lipoprotein (LDL) and observed effect was significantly affected by the initial lipid status of participants. Larger studies are necessary to gain additional insights on the clinical significance of ALA as an antioxidant and hypolipemic agent and to optimize its potential application in LSIL treatment.

High salinity in soil affects the normal growth and development of crop. MicroRNA171 (miR171) plays a momentous role in plant resistance to adversity stress. The functions of Sly-miR171d and Sly-miR171e on growth of tomato and fruit nutritional quality under salt stress were studied. The results showed that the seed germination and seedling growth of tomato that silencing of Sly-miR171d/e by short tandem target mimic (STTM) were better than those of wild-type (WT). Silencing of Sly-miR171d/e seeds maintained a high germination rate, while WT tomato seeds barely germinated under 50 mM NaCl stress. Under 100 mM NaCl stress, silencing of Sly-miR171d/e increased the expression of their target gene SlGRAS24 with increasing duration of salt stress, while they enhanced the antioxidant activity, reduced reactive oxygen species (ROS) accumulation, and enhanced the salt tolerance of tomato plant by regulating gibberellin (GA) level. Moreover, the preharvest and postharvest fruit nutritional quality under salt stress was also studied, and the results showed that silencing of Sly-miR171d/e increased soluble solids, lycopene, total carotenoids, Vitamin C, organic acids and phenolic substances in tomato fruit. The present study demonstrated that silencing of Sly-miR171d/e enhanced plant salt tolerance through the GA pathway, and improved fruit nutritional quality under salt stress.

With the rapid development of the economy and the transformation of social contradictions, the economic development model in China has also changed, and a high-quality economic development model has been proposed. This paper uses the entropy method to measure the level of innovation and high-quality economic development in nine prefecture-level cities in Fujian Province from 2011 to 2020, and finds that the overall level shows an increasing trend year by year, but there are obvious regional differences. By constructing a grey correlation degree model, it is found that the innovation subsystem and the high-quality economic development are strongly correlated, the development level of innovation and the subsystem of high-quality economic development are both strongly correlated, the grey correlation degree from high to low is green, coordinated, shared, and livelihood, indicating that innovation has a significant impact on high-quality economic development. Based on the research results, countermeasures and suggestions are proposed to promote the coordinated development of innovation and high-quality economy.

The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and failure to find quality issues in a timely manner within manufacturing settings may trigger the propagation of defects downstream. Currently, most of the inspection quality methods are direct measurement followed by manual judgment. The integration of deep learning methods provides a feasible way to identify defects in a timely manner, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model around discrete manufacturing characteristics, and use fuzzy theory to evaluate the quality level of production stages. Building Multivariate Long and short-term memory learning hidden quality representations to extract predictions from multi-level inspection data in manufacturing systems. Finally, by validating the data of actual produced water dispensers according to three evaluation indexes, RMSE, MAE, MAPE, the results show that Multivariate Long and short-term memory has better prediction performance.

The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.

Air pollution and the increasing production of greenhouse gases has prompted greater use of renewable energy sources; the EU has set the use of green energy at 32 percent by 2030. With this in mind, in the last 10 years the demand for pellets in Italy has more than doubled making Italy the second largest consumer in Europe. The quality of the pellets burned in stoves is crucial to indoor and outdoor pollution. Among other parameters, moisture and ash are used to classify pellets according to EN ISO 17225:2014. This work involved the analysis of the organic and inorganic fraction of both some finished products on the Italian market and some raw materials (e.g., wood chips) sampled according to the technical standard EN 14778:2011. The analytical results showed the presence of some substances potentially harmful to human health such as formaldehyde, acetone, toluene and styrene for the organic fraction and nickel, lead and vanadium for the inorganic fraction. The chemometric approach showed that it is the inorganic fraction which is most responsible for the diversification of the samples under study. The detection of some substances may be a warning bell about the impact of such materials both for the environment and for the human health.

The study of man-land relationship in urbanization process is the current frontier and focus of international research. How to balance urban development and ecosystem conservation in the Upper Yellow River is a key issue for sustainable development in China. In this study, we evaluated the Lanzhou-Xining urban agglomeration （LXUA）by constructing a multi-dimensional assessment system for urbanization quality and ecosystem services. The efficacy function model, entropy weight method, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used to quantitatively assess the subsystems' state of development. Then, the coupling model (CD) and the coordination degree（CCD）model were used to explore the coupling coordination relationship and spatial-temporal change characteristics of the composite system. The findings indicate that: 1）In 2020, the quality of urbanization in LXUA showed the pattern of "double core". The development of urban centers in each city is insufficient, and the proportion of counties with low-level is too high. 2）Integrated ecosystem services showed an increasing distribution pattern from the northeast to the southwest. Water provision services, soil conservation services and carbon fixation services all showed growth trends. 3）Each county’s composite system was in the run-in stage or highly coupled stage. The subsystems were closely related to each other. 4）The CCD was decreased by 6% between two decades. The number of counties on the verge of disorder was the highest. About 80% of the counties are relatively lagging behind in ecosystem services.

Lagoons are becoming sporadically utilised albeit they are equipped with high potential for the outsourcing of environmental and industrial benefits. It leads to the endangered pollution of lagoon water aquatic system. The prime reason is the lack of knowledge among stakeholders and researchers regarding the influential parameters in establishing the lagoon water quality. The optimal quality for lagoon water is critical for the longevity of aquatic ecosystem. This study focuses on using bibliographic references to find the most influential factors determining the water quality in lagoons for deriving a comprehensive long-term water quality monitoring plan to ensure the sustainability of the lagoon water ecosystem. The lagoon water quality was classified in this study into biological, physical, and chemical parameters and studied for their importance upon enhancing the water quality. Experiments were conducted on selected parameters using the water samples from the selected Sri Lankan lagoons with available facilities to observe the water quality. The overall findings on physiochemical and biological characteristics using the experiments temperature, turbidity, pH, salinity, DO, BOD, COD, phosphates, nitrates, ammonia content and faecal coliforms in water specimens suggest that the selected Sri Lankan lagoons are heavily polluted due to their distinctive variations from the allowable threshold limits specified in the literature sources. To increase such increased risks associated with lagoon water systems, a long-term monitoring strategy is recommended to be incorporated at lagoon waters in order to assure their sustainability.

The RMA11 water quality model, which has been previously calibrated and validated, was coupled with the RMA10 3D hydrodynamic model to evaluate sanitation scenarios in Buenaventura Bay. The bay receives direct discharge of untreated wastewater from 500,000 people through 695 outlets along the coast. Five different effluent reduction scenarios were proposed and compared based on fecal coliform concentration as an indicator. The areas where values exceed the standard for primary contact were also used to evaluate the scenarios. The model results indicate poor water quality in the bay and how that the situation will become even more severe if immediate action is not taken. The proposed reduction in discharges in stages will have temporarily more severe effects than the current situation, but these will end when the treatment plant becomes operational. However, even with the plant in operation, complete sanitation of the bay cannot be achieved, and further measures must be considered.

With Direct Laser Writing (DLW) maturing in all aspects as a manufacturing technology a toolset for quality assurance must be developed. In this work we want to introduce a first of its kind test artifact. Test artifacts are standardized 3D models with specific geometric feature to evaluate the performance of writing parameters. Test artifacts are already common in other 3D additive manufacturing technologies e.g. Selective Laser Melting. The test artifact introduced in this work was developed in particular to accommodate 1) the high geometrical resolution of DLW structures and 2) the limited possibilities to examine the resulting structure. Geometric accuracy, surface adhesion as well as confocal raman spectroscopy results were considered when evaluating the design of the test artifact. We will explain the individual features and design considerations of our DLW test artifact. The difference between two slicers, Cura and 3DPoli, and the implications on measured feature sizes and the general shape is quantified. The measured geometries are used to derive a general design guide for a specific combination of photoresist, laser power and scanning speed and to analyse the geometric accuracy of a structure produced using these guidelines.

Indoor crop cultivation systems such as vertical farms or plant factories necessitate artificial lighting. Light spectral quality can affect plant growth and metabolism and, consequently, the amount of biomass produced and the value of the produce. Conflicting results on the effects of light spectrum in different plant species and cultivars make it critical to implement a singular lighting solution. In this study we explored the response of green and red leaf lettuce cultivars (’Aquino’, CVg, or ‘Barlach’, CVr, respectively) to long-term blue-enriched light application (WB). Plants were grown for 30 days in a growth chamber with optimal environmental condi-tions (temperature: 20°C, relative humidity: 60%, ambient CO2, Photon Flux Density (PFD) of 260 µmol m-2 s-1 over an 18-h photoperiod). At 15 days after sowing (DAS) white spectrum LEDs (WW) were compared to WB (λPeak = 423 nm) maintaining the same PFD of 260 µmol m-2 s-1. At 30 DAS, both lettuce cultivars resulted adapted to the blue light variant, though the adaptive re-sponse was specific to the variety. Rosette weight, light use efficiency and maximum operating efficiency of PSII photochemistry in the light, Fv/Fm’, were comparable between the two light treatments. Significant light quality effect was detected on stomatal density and conductance (20% and 17% increase under WB, respectively, in CVg) and, on the modified anthocyanin re-flectance index (mARI) (40% increase under WB, in CVr). Net photosynthesis response was gen-erally stronger in CVg compared to CVr; e.g. net photosynthetic rate, Pn, at 1000 µmol m-2 s-1 PPFD increased from WW to WB by 23% in CVg, compared to 18% in CVr. Results obtained suggest the occurrence of distinct physiological adaptive strategies in green and red pigmented lettuce cultivars to adapt to the higher proportion of blue light environment.

This paper directly addresses a long-standing issue that affects the development of many complex distributed software systems: how to establish quickly, cheaply, and reliably whether they can deliver their intended performance before expending significant time, effort and money on detailed design and implementation. We describe ∆QSD, a novel metrics-based and quality-centric paradigm that uses formalised outcome diagrams to explore the performance consequences of design decisions, as a performance blueprint of the system. The distinctive feature of outcome diagrams is that they capture the essential observational properties of the system, independent of the details of system structure and behaviour. The ∆QSD paradigm derives bounds on performance expressed as probability distributions encompassing all possible executions of the system. The ∆QSD paradigm is both effective and generic: it allows values from various sources to be combined in a rigorous way, so that approximate results can be obtained quickly and subsequently refined. ∆QSD has been successfully used by Predictable Network Solutions for consultancy on large-scale applications in a number of industries, including telecommunications, avionics, and space and defence, resulting in cumulative savings worth billions of US dollars. The paper outlines the ∆QSD paradigm, describes its formal underpinnings, and illustrates its use via a topical real-world example taken from the blockchain/cryptocurrency domain. ∆QSD has enabled challenging throughput targets to be met for a globally distributed blockchain operating on the public internet.

(1) Background: Investigating the characteristics of photosynthetic physiological changes of leaves in Mangifera indica L. cv. 'GuIfei' under enhanced UV-B radiation, natural light exposed trees were regarded as control, and 96 kJ·m-2·d-1enhanced UV-B radiation was artificially simulated in the field; (2) Methods: The changes of fruit maturity and fruit quality, leaf net photosynthetic rate (Pn), photosynthetic pigments contents, photochemical reaction, activities of photosynthetic enzymes and their genes expressions were determined; (3) Results: Compared with CK, the percentage of mature fruits of the treatment was significantly increased, and fruit quality was better. The net photosynthetic rate (Pn), the contents of photosynthetic pigment, Hill reaction activity and photochemical quenching coefficient (qP) of the treatment leaves showed a significantly higher trend than CK. The activities of Rubisco and RCA, and the expression of Rubisco genes rbcL and rbcS were significantly increased; (4) Conclusions: 96 kJ·m-2·d-1 enhanced UV-B radiation treatment improved Rubisco activity through increasing the expression of Rubisco genes rbcL and rbcS, thereby enhancing the CO2-fixing capacity and dark reaction capacity of leaves. Based on this, it raised the net photosynthetic rate of leaves, which promoted the early maturity of 'Guifei' mango by the fast accumulating photosynthetic products.

The article reviews the constituent’s physico-chemical parameters in the water of the Mansagar Lake of the Jaipur City. The water quality is also investigated using evaluated physico-chemical parameters with in-depth study of their distribution as per sample location-wise and season-wise. A detailed discussion on the associationship among parameters (pH, Conductivity, turbidity, dissolved Oxygen, chemical and Biochemical Oxygen demand, etc.) using the Pearson’s analytical method is also presented. The strength of the article is the graphical presentation of evaluated physico-chemical parameters and the water quality indices for sample station-wise and the season-wise. To our best of understanding, first time such details have been clubbed together and discussion in brief for studied lake.

The monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered satisfactory (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than the ones positioned along the mainstream. IDW was the most chosen model for data imputation.

Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires the advanced equipment and chemical knowledge of certified laboratories, and has therefore a limited accessibility. In this work a minimalist, portable and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential of substituting expensive and complex chemical analysis.

Application of exogenous jasmonate can stimulate the production of ethylene, carotenoids and aroma compounds, resulting in the acceleration of fruit ripening. These alterations improve fruit quality and make fruit desirable for human consumption, but overripening of a fruit results in large losses of fruit crops. In order to overcome this problem, 1-methylcyclopropene was ap-plied to the fruits due to its capacity to block the receptors of ethylene, resulting in the sup-pressed of fruit ripening. In this study, treatments only with 1-methylcyclopropene, and with both 1-methylcyclopropene and methyl jasmonate was conducted to observe if an exogenous methyl jasmonate can improve the levels of metabolites in their fruits with ethylene receptors blocked. Fruits were analyzed at 4, 10 and 21 day after harvest (DAH) and compared with the no treated fruits. The postharvest treatments affected primary metabolites (sugars, organic acids, amino acids and fatty acids) and secondary metabolites (carotenoids, tocopherols and phytoster-ols). However, the lipid metabolism of the tomato was the most impacted by the exogenous jasmonate. Fatty acids, carotenoids, tocopherols and phytosterols showed a delay in their pro-duction at 4 and 10 DAH. In contrast, at 21 DAH these non-polar metabolites exhibited an im-portant improvement in their accumulation.

This research is aimed at evaluating two different scenarios, firstly, appraising the impacts of employing the concepts of Total Quality Management (TQM) to the construction projects in Saudi Arabia. The results of the study were obtained through utilization of a descriptive analytical approach, where 300 questionnaires were distributed to engineering firms and companies with a response rate of 200 questionnaires, hence achieving the study sample for this research. The data gathered was analyzed by applying the Statistical Package for Social Science (SPSS) program and calculating the Relative importance index (RII) and the mean values. From the research conducted, the outcomes showed that the management’s ability to commit using TQM while applying BIM obtained a relative importance of (0.717), while the relative importance for the management’s ability to commit using TQM without the application of BIM is (0.552). The results showed that construction projects in Saudi Arabia still sustain setbacks from applying TQM concepts and suffer from the lack of administrative, scientific and technical applications. In a second scenario, a hybridized support vector regression (SVR) Harris-hawks optimization (HHO) (i.e., SVR-HHO) were used to predict the TQM. The performance accuracy of the models was checked through three different evaluation metrics namely; mean square error (MSE), correlation co-efficient (CC) and Nash-Sutcliffe efficiency (NSE). the hybridized emerging SVR-HHO outperformed the other two data driven approaches in both the training and testing stages based on the employed evaluation metrics. Overall, the obtained results showed that both the machine learning and metaheuristic approaches were capable of predicting TQM.

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of the people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

The 2019-2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive extend of the wildfires has caused smoke pollutants transported not only to New Zealand but across the Pacific Ocean to South America. At the height of the wildfires, smoke plumes were injected into the stratosphere at height up to 25km and hence transported across the globe. Based on meteorological and air quality simulation using WRF-Chem model, air quality monitoring data collected during the bushfire period and remote sensing data from MODIS and CALIPSO satellites, the extend of the wildfires and the pollutant transport, and their impacts on air quality and health on exposed population in NSW can be analysed. The results showed that WRF-Chem model using Fire Emission Inventory from NCAR (FINN) predicts the dispersion and transport of pollutants and the predicted concentration of PM2.5 and other pollutants from wildfires reasonably well when compared with ground-based and satellite data. The impact on health endpoints such as mortality, respiratory and cardiovascular diseases hospitalisation across the modelling domain is then estimated. The estimated health impact is comparable with previous study based only on observation data, but the results in this study provide much more detailed spatially and temporally with regards to the health impact from the 2019-2020 wildfire.

This is an article or technical note which is intended to provides an insight journey of Machine Learning Systems (MLS) testing, its evolution, current paradigm and future work. Machine Learning Models, used in critical applications such as healthcare industry[1], Automobile [2], [3] and Air Traffic control, Share Trading etc., and failure of ML Model can lead to severe consequences in terms of loss of life or property. To remediate this, developers, scientists, and ML community around the world, must build a highly reliable test architecture for critical ML application. At the very foundation layer, any test model must satisfy the core testing attributes such as test properties and its components. This attribute comes from the software engineering [5], [6], but the same cannot be applied in as-is form to the ML testing and we will tell you “why”.

Three-phase AC electric arc furnace (EAF) is a typical non-linear load, causing many power quality problems. Most of the researches on the voltage problems of EAF mainly focus on the voltage fluctuation, and less on the transient voltage problems caused by EAF short circuit and open circuit. In this paper, the relationship between voltage and current of EAF is obtained by combining hyperbolic function and exponential function, then the white noise and chaotic circuit are added to establish the EAF model which is suitable for the study of voltage fluctuation and transient voltage. This paper analyzes the causes of the transient voltage problem of the EAF, calculates the short-circuit current, reactive impact and the influence on voltage at the point of common coupling (PCC) in the three-phase short-circuit of the EAF, and compares the calculation results with the simulation results to prove the accuracy of this model. The results show that the reactive impact of three-phase short circuit is about twice as much as that of normal operation of EAF, resulting in about 30% voltage sag at the PCC, which is very unfavorable to the power grid. This paper provides reference for transient power quality evaluation and dynamic reactive power compensation of EAF.

Livestock production systems generate nuisance odor and gaseous emissions affecting local communities and regional air quality. Also, there are concerns about the occupational health and safety of farm workers. Proven mitigation technologies that are consistent with the socio-economic challenges of animal farming are needed. We have been scaling up the photocatalytic treatment of emissions from lab-scale, aiming at farm-scale readiness. In this paper, we present the design, testing, and commissioning of a mobile laboratory for on-farm research and demonstration of performance in real farm conditions. The mobile lab is capable of treating up to 1.2 m3·s-1 of air with TiO2-based photocatalysis and adjustable UV-A dose based on LED lamps. We summarize the main technical requirements, constraints, approach, and performance metrics for the mobile laboratory, such as the effectiveness (measured as the percent reduction) and cost of photocatalytic treatment of air. The commissioning of all systems with standard gases resulted in ~9% and 34% reduction of NH3 and butan-1-ol, respectively. We demonstrated that as the percent reduction of standard gases increased with increased light intensity and treatment time. These results show that the mobile laboratory was ready for on-farm deployment and evaluating the effectiveness of UV treatment.

Dust storms originating from Central Australia and western New South Wales frequently cause high particles concentration at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affect air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model – Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints namely mortality, respiratory and cardiac diseases hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. The top five Statistical Area Level 4 regions with the most impact in term of mortality, respiratory diseases hospitalisation and cardiac disease hospitalisation are Far West and Orana, Newcastle and Lake Macquarie, New England and North West, Sydney – Inner South West and either Central Coast (mortality) or Sydney – Parramatta (respiratory diseases hospitalisation) or Sydney – Inner West (cardiac diseases hospitalisation). Generally, the health impact is similar in order of magnitude to that caused by biomass burnings events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory diseases hospitalisation for this dust event (161) compared to fire event (24).

Citizen science has the potential to support the delivery of the United Nations Sustainable Development Goals (SDGs) through its integration into national monitoring schemes. In this study, we explore the opportunities and biases of citizen science (CS) data when used either as a primary or secondary source for SDG 6.3.2 reporting. We use data from waterbodies that have both CS and regulatory monitoring in England and Zambia to explore their biases and complementarity. A comparative analysis of regulatory and CS data provided key information on appropriate sampling frequency, site selection and measurement parameters, necessary for more robust SDG reporting. The results show elevated agreement for pass/fail ratios and indicator scores for English waterbodies (80%) and demonstrate CS data can improve granularity and spatial coverage for SDG indicator scoring, even when extensive statutory monitoring programmes are present. In Zambia, management authorities are actively using citizen science projects to increase spatial and temporal coverage for SDG reporting. Our results indicate that design considerations for SDG focused citizen science can address local needs as well as provide a more representative indicator of the state of a nation’s freshwater ecosystems for international reporting requirements.

Stakeholder input into the decision-making process when developing public health programs and policies is crucial. This article presents an innovative approach, involving online participation with a wide group of stakeholders located in different geographic locations, for policy consensus by research methodology. The results of the project have been used to propose assumptions regarding a strategy for preventing blood-borne diseases in Poland. The research was conducted iteratively using a multi-stage qualitative methodology to explore risk assessment involving blood-borne infections. The final output of the entire project is a list of key problems/challenges and solutions associated with medical and nonmedical services that are connected to the breakage of tissue continuity. Qualitative research is rare in risk assessment as priority is usually given to statistical data and end-points (quantitative studies ). Statistical data on health risk allows to assess the health implications, while the methodology applied also allowed diagnosis of the processes leading to these effects. In addition to policy preparation for blood-borne illnesses, the methodology employed in the study can also be used to successfully explore other areas of public health policy decisions.

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for high-quality digitization of content from pages and whiteboards to sharable online material. But capturing this information is not always easy, nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts, by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use.

Recent advancements in web-based geospatial software and smartphone technology have popularized the process of voluntary production and sharing of geospatial data by individual citizens. Through such Volunteered Geographic Information (VGI) activities, people across the world participate in online mapping projects (such as OpenStreetMap) to insert their spatial information. The quality of data generated by such VGI activities has profound impacts on online mapping projects and their spatial database. In this study, we examine the VGI contribution pattern in OpenStreetMap through three case study neighborhoods located in three major cities: Tehran, London, and Los Angeles, and investigate how it might affect the process of quality assessment of VGI.