Symmetry-breaking is a phenomenon that is observed in various contexts, from the early universe to complex organisms, and is considered a key puzzle in understanding the emergence of life. The presence of enantiomeric amino acids and proteins highlights its critical role. However, the origin of symmetry-breaking has yet to be comprehensively explained, particularly from an energetic standpoint. This article explores a novel approach by considering energy dissipation, specifically lost free energy, as a crucial factor in elucidating symmetry-breaking. Through a thorough thermodynamic analysis applicable to enantiomeric systems at various scales, from elementary particles to aggregated matter like crystals, we present experimental findings that establish a direct correlation between non-equilibrium free energy and energy dissipation during formation processes. Results emphasize the pivotal role of energy dissipation not only as an outcome but as the trigger for symmetry-breaking. This insight suggests that understanding the origins of complex systems, from cells to living beings and the universe itself, requires a lens focused on non-equilibrium processes, ultimately facilitating the formation of non-racemic systems.

Afghanistan is overwhelmed with food insecurity that is a large percentage of the population lacks reliable access to food supplies. Grains play a vital role in the country's food security due to their importance in terms of consumption and production quantity. This paper estimates cereal self-sufficiency and then makes a food balance forecast of the three major food crops cultivated in Afghanistan over six-decades. Descriptive statistics and coarse metric techniques were employed to determine cereal self-sufficiency and food balance forecast. The econometric analysis demonstrated that (1) Afghanistan is not yet self-sufficient in meeting grain consumption, and the production and consumption ratio declined from 0.9 to 0.55 from 1979 to 2030. (2) The gap of theoretical food imbalance will increase, and by the year 2030, cereal production will likely be sufficient for only 49.8 percentage of inhabitants, leaving a high shortage equivalent to the amount required by 24.4 million people. By evaluating the quantitative food balance and the growing population change, this study presents an analysis of the emerging threat to Afghanistan's food security and suggests a solution for food security, should increase the size of public agricultural expenditure, improve the level of agriculture infrastructure, and continue to introduce a policy to achieve higher yield.

The capacitor-mismatch among diverse defects caused by the manufacturing process variations significantly affects the linearity of capacitor-array used to implement the capacitive digital-to-analog converter (CDAC) in the successive-approximation-register (SAR) analog-to-digital converter (ADC). Accordingly, the linearity of the SAR ADC is limited by that of capacitor-array, resulting in serious yield loss. This paper proposes an efficient foreground self-calibration technique to enhance the linearity of the SAR ADCs by mitigating the capacitor-mismatch based on the split ADC architecture along with variable capacitors. In this work, two ADC channels (i.e., ADC1 and ADC2) for the split ADC architecture include their capacitive DACs (CDACs) whose binary-weighted capacitor-arrays consist of variable capacitors. A charge-sharing SAR ADC is used for each ADC channel. In the normal operation mode, their digital outputs are averaged to be the final ADC output, as in a conventional split ADC. In the calibration mode, every single binary-weighted capacitor for the two ADCs is sequentially calibrated by making parallel or/and antiparallel connection among two or thee capacitors from the two channels. For instance, because the capacitors of the CDACs ideally exhibit the binary-weighted relation as Cn=2×Cn−1, variable capacitor Cn of ADC1 can be updated to be closest to the sum of Cn−1 of ADC1 and Cn−1 of ADC2 for the calibration. For the process, the two capacitor-arrays of the two ADCs can be reconfigured to be connected to each other, so that Cn of ADC1 can be connected with two of Cn−1 of ADC1 and ADC2 in antiparallel. The two voltages at the top and the bottom plates of the CDAC are compared by a comparator of ADC1, and the comparison results are used to update Cn. This process is iterated, until Cn is in agreement with the sum of two of Cn−1. Finally, all the capacitors can be calibrated in this way to have the binary-weighted relation. The simulation results based on the proposed work with a split SAR ADC model verified that the proposed technique can be practically used, by showing that the total-harmonic-distortion and the signal-to-noise-and-distortion ratio were enhanced by 21.8-dB and 6.4-dB, respectively.

Contemporary diagnostic practice in psychiatry relies on the tick-box approach of diagnostic manuals. In light of the obvious limitations of diagnostic systems, narrative formulations, whether in the form of biological, psychodynamic or behavioural cognitive ones, have been proposed. Their approach, which can be caricatured as one of drawing arrows, enriches our clinical tools and practice but in their explicitly theoretical orientation, as well as expanding the field of discovery, they also constrain it. In two previous papers I summarized the resulting limitations, with specific reference to the patient’s experience and proposed further enriching clinical perspicacity by attending to images and parataxis. The specific intent of attending to the patient’s experiences through a process that explicitly eschews narrativization, but allows clinical phenomena to “hang loose”, is to facilitate a process of clinical discovery and healing through practices of co-production between patient and clinician. In the present paper, I expand the range of phenomena that may assist in this co-production through adding attention to dreams to that of images and parataxis. In particular, I turn to the work of the Italian novelist Guido Morselli. Morselli agrues that the self is created through bundling experiences together through acts of “will” in order to establish our personal narrative. In doing so, however, the “will” forbids access to phenomena that do not fit the dominant narrative. Experience is therefore impoverished. Dream consciousness is a type of consciousness not harnessed by the constraints of narrativity. It allows a slipping of the narrative vigilance which bundles phenomena together to create a coherent narrative and thus offers a richer menu of potential experiences, including “bizarre” images that can serve to challenge our usual self-narrative and offer a new perspective on ourselves.

In this paper a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM)and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.

Climate change is likely to have a complex effect on the growth of plants, their phenology, plant-pollinator interactions, and reproductive success. Therefore, we tested the impact of three key factors (temperature, water, and nitrogen supply) on traits, pollination, and seed production in Sinapis alba (Brassicaceae). We grew the plants in different combinations of temperature, water, and nitrogen supplementation, measured multiple vegetative and floral traits, and assessed the response of pollinators in the field. We also evaluated the effect of growing conditions on seed set in plants exposed to pollinators and hand-pollinated plants. Our results show that water stress impaired vegetative growth, decreased flower production, reduced visitation by pollinators and seed set, while nitrogen availability played an important role in nectar production. Temperature modulated the effect of water and nitrogen availability on vegetative and floral traits and strongly affected flowering phenology and flower production. We demonstrated that changes in temperature, water, and nitrogen availability induce changes in plant vegetative and floral traits which impact flower visitation and consequently plant reproduction. Climate change, particularly increasing temperature combined with reduced precipitation, thus may impact plant-pollinator interactions with negative consequences for the reproduction of wild plants and insect-pollinated crops.

Purpureocillium lilacinum PL11 (formerly Paecilomyces lilacinus) is a ubiquitous fungus found in several habitats, mainly in the soil, and belongs to the Ophiocordycipitaceae family. This fungus was shown to have potential applications in agriculture as a biocontrol agent and biofertilizer. P. lilacinum can inhibit plant‒pathogenic fungi and nematodes, including root-knot and potato cyst nematodes. Additionally, P. lilacinum produces siderophores and indole-3-acetic acid (IAA), enhancing plant development and increasing soil nutrient availability. P. lilacinum has been extensively studied in various commodities, including pineapple, maize, soybean, and legume. Most studies have evaluated P. lilacinum as a biocontrol agent, especially for nematode control. Other studies have evaluated the production of secondary metabolites and bioremediation, and few studies have used this fungus as a plant growth promoter. This review addressed important aspects of using this fungus. P. lilacinum is a promising fungus that can be used for agricultural production, reducing environmental impact, and thus collaborating for a sustainable agriculture production system.

The purpose of this study is to explore conceptual approaches in co-production studies and to examine current research trends of the study. The conceptual paper includes research articles related to co-production in public administration field. By thoroughly scrutinizing 32 research works of co-production, this study highlights major loopholes in the field of the study. The contributions of the study are: (1) identifying two common characteristics of co-production, (2) categorising three types of co-producing by end-users, and (3) finding that goals and success of co-production are more beneficial for service providers though its initial approach is citizen-centric approach. We suggest that future studies should be (1) to focus on reasons for co-production failures or success, (2) to discover further hindrances for co-production in service production, (3) to examine influencing factors on service providers as well as institutional impacts on co-production process, and (4) to include practical assessment in co-production study.

The authors of the article analyze the existing methods and models of technological preparation of machine-building industries. The structure of a three-level simulation model with network-centric control, the structures of individual elements of the simulation model and the process of simulation modeling are described. The criteria for choosing a rational option for the processing technological route have been determined. During the research, a simulation program was implemented in C++. It allows you to select the optimal scenario for the operation of a production site based on two criteria: time and cost. The volume of implementation is about 2x103 lines of code. A diagram of the modeling algorithm for the implemented program, a description of the classes and their interactions are given in the article. The developed simulation model was tested at a machine-building enterprise using the example of the “Pusher” part, manufactured under single-unit production condi-tions. The technological equipment used for the manufacture of this part was formed in the form of input data of the simulation model. The results of simulation modeling for the selected part are described. For each variant of the technological processing route, the values of variable costs and the duration of the production cycle were determined.

In Brazil, the production of corn and soybeans has been growing significantly in recent years. Considering that the strategies for investing in the production of these crops are conditioned by production costs and risk, the objective of this article is to propose a simulation model that indicates the trend of production costs for these commodities, considering the dispersion and correlation of selected key variable prices and corn and soybean production costs practiced between the years of 2018 and 2022. Fifty specialized companies in the state of Sao Paulo, Brazil, provided data for this study, and Monte Carlo simulations showed that the total cost of corn production/ha is between USD 600.00 and USD 1150.00, with a level of certainty of 84.7%, and soybeans are in the range of USD 260.00 to USD 420.00, with a level of certainty of 86.4%. The model evidenced a trend of decreasing production costs for the crops for the 2023/24 and 2024/25 harvests, as the input with the greatest impact (fertilizer) is showing a downward trend. On the other hand, costs related to labor, soybean seed, and fungicides are showing an upward trend, while dolomitic limestone corrective remains stable.

Background: Student-specific stress is pervasive and impairs health and attainment. Experimentally induced self-affirmation can reduce stress, but little is known about the mechanisms underlying this effect, and the role of individual differences. The present study explores the relationship between spontaneous self-affirmation, student-specific stress, and overlapping constructs of positive self-regard during the COVID-19 pandemic. Methods: The study investigated inter-relationships between spontaneous self-affirmation, student-specific perceived stress and potential covariates including self-esteem, self-integrity and habitual positive self-thought. University students (N = 151) aged 18 to 50 responded to an online questionnaire including the Spontaneous Self-Affirmation Measure, College Student Stress Scale, Rosenberg Self-Esteem Scale, Habitual Index of Positive Thinking and Self-Integrity Scale. Results: Unexpectedly, regression analysis revealed that spontaneous self-affirmation was a positive predictor of student-specific stress, seemingly driven by relational affirmations. Self-esteem and habitual self-thought were negative predictors of student-specific stress, whilst self-integrity was not a predictor. Conclusions: Habitual positive self-thought may offer a sustainable solution to student-specific stress, but spontaneous self-affirmation does not. A reduction in defensive responses, elicitation of negative emotions, or use of conditional relationships in relational affirmation

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

In this paper I tentatively answer 50 questions sampled from a pool of over 10,000 weekly questions formulated by students in a course entitled “The Self”. The questions pertain to various key topics about self-processes, such as self-awareness, self-knowledge, self-regulation, self-talk, self-esteem, and self-regulation. The students’ weekly questions and their answers highlight what is currently know about the self. Answers to the student questions also allow for the identification of some recurrent lessons about the self. Some of these lessons include: all self-processes are interconnected (e.g., prospection depends on autobiography), self-terms must be properly defined (e.g., self-rumination and worry are not the same), inner speech plays an important role in self-processes, controversies are numerous (are animals self-aware?), measurement issues abound (e.g., self-reflection as an operationalization of self-awareness), deficits in some self-processes can have devastating effects (e.g., self-regulatory deficits may lead to financial problems), and there are lots of unknowns about the self (e.g., gender differences in Theory-of-Mind).

Companies are increasingly focusing on sustainable business practices. Internal and external stakeholders’ expectations manifest in legal requirements, national and international standards, and market and customer expectations, among other things, must be considered. In addition to profit maximization as the usual target for corporate management, management must consider environmental sustainability aspects such as resource efficiency, greenhouse gas intensity, and a company's emissions behavior. In addition, social aspects relating to the company's employees, the immediate urban environment, the situation in the supply chain, and effects on the market environment must increasingly be considered. Specifically, companies are faced with the chal-lenge of dealing with conflicting objectives regarding the various aspects of sustainability and, if necessary, weighing them up against each other. These trade-offs must be made against the company's socio-economic and ecological environment, corporate strategy, and sustainability goals. This paper provides an overview of current approaches and research gaps on this topic through a literature review. It shows a lack of methods and frameworks to specifically deal with trade-offs and conflicts of goals.

Vitrimers are covalent network materials, comparable in structure to classical thermosets. Unlike normal thermosets, they possess a chemical bond swap mechanism that makes their structure dynamic and suitable for activated welding and even autonomous self-healing. The central question in designing such materials is the trade-off between autonomy and material stability: The swap mechanism facilitates the healing but it also facilitates creep, which makes the perfectly stable self-healing solid a hard goal to reach. Here, we address this question for the case of self-healing vitrimers made from star polymers. Using coarse-grained molecular dynamics simulations, we study the adhesion of two vitrimer samples and find that they bond together on timescales that are much shorter than the stress relaxation time. We show that the swap mechanism allows the star polymers to diffuse through the material through coordinated swap events, but the healing process is much faster and does not depend on this mobility.

In Ghana, rice remains a critical crop, representing 15% of the country's GDP. However, production has been hindered by limited water access, degraded soil, pests and diseases, and ineffective pesticide use. These issues have impeded industry growth while adversely affecting the environment and impairing socioeconomic development. To combat these challenges, it is necessary to implement sustainable production strategies that emphasize environmental protection, resource management, and socioeconomic progress. This study evaluates sustainable rice production in Ghana, taking into account its consequences on the environment, socioeconomic growth, and food security. It pinpoints gaps and offers advice for stakeholders, policymakers, and scholars to transition to sustainability. The study illuminates the increasing significance of rice in Ghana and its role in food security, illustrating increased output due to widened land rather than higher yields. It underscores the necessity of fulfilling surging demand while implementing environmentally friendly practices. The paper scrutinizes the difficulties encountered by the rice sector, such as restricted water supplies and soil degradation, along with the adverse impacts of pests, diseases and inefficient pesticide utilization. Sustainable methods are imperative for Ghana's agribusiness, environmental protection, and socioeconomic progress. By embracing green techniques, prioritizing resource management, and investing in research, Ghana can surmount production issues. This review provides invaluable insight and suggestions for policymakers, academicians, and stakeholders alike to ensure sustainable rice production for current and future generations.

In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry with numerous applications which guide engineers in better decision-making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time and computer resources associated costs, rendering reservoir simulators as not fast and robust enough, thus introducing the need for more time-efficient and smart tools, like ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for the cases of individual simulation runs and history matching, whereas the ML-based Production Forecast Optimization applications will be presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

Being able to complete the production of the product project on time is the prerequisite to win more tasks, many enterprises have begun to study intelligent scheduling algorithms to achieve dynamic control of the total project duration. However, the production operation time of products is highly volatile, and it is impossible to predict the possible operation time of future products. In the actual production environment, we often encounter extreme situations such as mixed production lines, multiple varieties, variable batches, etc. A dynamic optimization CPM model in the ERP/MES system with the attention mechanism (EMA-DCPM) will collect, extract and predict the operation time through artificial intelligence technology, and it can accurately predict the exact operation time of a batch of products that have not yet been produced. Through experiments, our algorithm can realize the production scheduling and get more than 200% better precision than former algorithms and realize dynamic optimization of complex projects.

Based on the survey data of 5 large grain-producing provinces in China, we studied the promo-tion effect of the agricultural production trusteeship on high quality production of grain by using a propensity score matching method. The empirical results show that the high-quality production of grain level increased by 0.292, with an increase of 87.4% after participating in agricultural production trusteeship. Among them, the level of high efficiency, premiumization, greenization and branding of grain production has respectively increased by 0.234, 0.373, 0.208, and 0.158. However, there are differences in the facilitation effects of different trusteeship services, with the best promotion effect of agricultural material supply services, followed by post-harvest services, and then land management services and cultivating and harvesting services, the ATT is 0.287, 0.230, 0.158, and 0.139, respectively. Meanwhile, there are differences in the promotion effects for farm households with different factor endowments. The promotion effect is better for small farm households with 3 laborers or less, a land management scale of 10 mus or less, and 1 type of ag-ricultural machinery or less. Therefore, in order to give full play to the promotion effect of agri-cultural production trusteeship on high quality production of grain, the government should vigorously support its development and guide more smallholders to choose agricultural produc-tion trusteeship.

Self-assembly is a spontaneous process through which macroscopic structures are formed from basic microscopic constituents (e.g. molecules or colloids). By contrast, the formation of large biological molecules inside the cell (such as proteins or nucleic acids) is a process more akin to self-organization than to self-assembly, as it requires a constant supply of external energy. Recent studies have tried to merge self-assembly with self-organization by analyzing the assembly of self-propelled (or active) colloid-like particles whose motion is driven by a permanent source of energy. Here we present evidence that points to the fact that self-propulsion considerably enhances the assembly of polymers: self-propelled molecules are found to assemble into polymer-like structures, the average length of which increases towards a maximum as the self-propulsion force increases. Beyond this maximum, the average polymer length decreases due to the competition between bonding energy and disruptive forces that result from collisions. The assembly of active molecules might have promoted the formation of large pre-biotic polymers that could be the precursors of the informational polymers we observe nowadays.

The objective of this work was to evaluate the blending and grinding influence over the nutritional, sensorial and sustainable aspects of coffee. To do so, a systematic review of the literature was accomplished. The database for the selection of relevant papers was the Portal de Periódicos da Capes, with remote access via CAFe. For the elaboration of the research, a chronological criterion with period restriction was used, considering the period between 2008-2022, to access all possible works related to the theme of this work. The following terms were used: blending; grinding; coffee; nutrition-al; sensorial; sustainability. To filter the searches, the association of these terms was also used by means of links and word associations. In the terminology, the Boolean operator AND was used to interconnect the terms used. Roasting degree, grinding, and amount of each coffee species impacts the nutritional and sensorial aspects of coffee. And the determination of each blending influences the sustainability of the environment, economic and social aspects of the coffee chain.

A replicated outdoor pot experiment was conducted in order to investigate the effect of different phytohormone and siderophore producing, and P-solubilizing bacterial species on spinach nu-trient uptake, nitrate concentration and nitrate reductase activity. The mentioned parameters were determined in spinach leaves (Spinacia oleracea L.), non-inoculated and inoculated with four plant growth–promoting actinobacteria (Streptomyces griseus (S1), Streptomyces albogriseolus (S2), Strep-tomyces aurantiacus (S3) and Streptomyces kanamyceticus (S4) under the influence of two sources of nitrogen fertilizers including potassium nitrate and urea (100 and 200 ppm). Inoculation with the strains increased spinach shoot fresh weight by 16%–43% over the control. Bacterial inoculation gave leaf chlorophyll increases of 15%–40%. Inoculation increased plant height by 2.2%–24.6% in spinach. A close reverse relationship between nitrate concentration and enzyme activity (r2= 0.87) was demonstrated. The measured parameter responses were variable and dependent on the in-oculant strain, with highest enzyme activity and lowest nitrate concentration exhibited in S2 (S. albogriseolus) inoculation. The source and application dose of nitrogen fertilizer had varied impact on measured parameter. The maximum phosphorous and iron concentration were measured by soil inoculation with S4 and applying 200 ppm nitrate potassium nitrogen fertilizer. Application of 200 ppm KNO3 nitrogen fertilizer with different Streptomyces strains showed the capability of S2 in decreasing nitrate content while protein content increased. In particular, the strains S2 and S4 have great potential in being formulated and used as biofertilizers.

As the oil industry increasingly turns to unconventional low-permeability and low-porosity reservoirs, precise post-fracture production prediction is a pivotal instrument in investment de-cision-making, formulation of energy policies, and promotion of environmental assessments. Nevertheless, despite extensive research spanning decades, the precise post-fracture production prediction based on logging parameters remains an intricate task. In this study, we gathered ex-tensive logging data and segmented the post-frac gas production during well testing on the first day to enrich the dataset. Nine pipelines were then architected using various techniques of data preprocessing, feature extraction, and advanced machine learning models. Hyperparameter op-timization was executed via the GridsearchCV. To assess the efficacy of diverse models, metrics including the coefficient of determination (R2), standard deviation (SD), and root mean square error (RMSE) were invoked. Among the several pipelines explored, the PS-NN exhibited excellent predictive capability in specific reservoir contexts (an R2 value of 0.94 and an RMSE of 48.15). In essence, integrating machine learning with logging parameters effectively assesses reservoir productivity at multi-meter formation scales. This strategy not only mitigates uncertainties en-demic to reservoir exploration but also equips petroleum engineers with monitoring on reservoir dynamics, thereby facilitating reservoir development. Additionally, this approach provides res-ervoir engineers with an efficient way of reservoir performance oversight.

Nanostructured transition metal nitrides (TMNs) have been considered a promising substitute for precious metal catalysts toward ORR due to their multi-electron orbitals, metallic properties, and low cost. To design TMNs catalysts with high catalytic activity toward ORR, the intrinsic features of the influencing factor on the catalytic activity toward ORR of nanostructured TMNs need to be investigated. In the paper, titanium nitride (TiN), zirconium nitride (ZrN), and hafnium nitride (HfN) nanoparticles (NPs) are high-efficiency and one-step synthesized by the direct current arc plasma. TiN, ZrN, and HfN NPs with an oxidation layer are applied as the catalysts of hybrid sodium-air batteries (HSABs). The effect of composition and structure attributes of TMNs on ORR catalysis is defined as follows. (ⅰ) Composition effect. With the increase of the oxygen content, the catalytic ORR capability of TMNs decreases progressively due to the reduction of oxygen adsorption capacity; (ⅱ) Structure effect. The redistribution of the density of states (DOS) of ZrN indicates higher ORR activity than TiN and HfN. HSABs with ZrN exhibit an excellent cyclic stability up to 137 cycles (about 140 h), an outstanding rate performance, and a specific capacity of 2817 mAh·g−1 at 1.0 mA·cm−2.

Syntrophic acetate oxidation operates close to the thermodynamic equilibrium and very little is known about the participating organisms and their metabolism. Clostridium ultunense is one of the most abundant syntrophic acetate-oxidising bacteria (SAOB) found in engineered biogas processes operating with high ammonia concentrations. It has been proven to oxidise acetate in cooperation with hydrogenotrophic methanogens. There is evidence that the Wood-Ljungdahl (WL) pathway plays an important role in acetate oxidation. In this study we analysed the physiological and metabolic capacities of C. ultunense on genome scale and conducted a comparative study of all known characterised SAOB, namely Syntrophaceticus schinkii, Thermacetogenium phaeum, Tepidanaerobacter acetatoxydans and Pseudothermotoga lettingae. The results clearly indicated physiological robustness beneficial for anaerobic digestion environments and revealed unexpected metabolic diversity with respect to acetate oxidation and energy conservation systems., Unlike S. schinkii and Th. phaeum, C. ultunense clearly does not employ the oxidative WL pathway for acetate oxidation, as its genome (and that of P. lettingae) lack important key genes. In both those species, a proton motive force is likely formed by chemical protons involving putative electron-bifurcating [Fe-Fe] hydrogenases rather than proton pumps. No genes encoding a respiratory Ech hydrogenase, as involved in energy conservation in Th. phaeum and S. schinkii, were identified in C. ultunense and P. lettingae. Moreover, two respiratory complexes sharing similarities to the proton-translocating ferredoxin:NAD⁺ oxidoreductase (Rnf) and the Na+ pumping NADH:quinone hydrogenase (NQR) were predicted. These might form a respiratory chain involved in reduction of electron acceptors other than protons. However, involvement of these complexes in acetate oxidation in C. ultunense and P. lettingae needs further study. This genome-based comparison provides a solid platform for future meta-proteomics and meta-transcriptomics studies and for metabolic engineering, control and monitoring of SAOB.

In this paper, I propose a test for machine self-awareness inspired by the Turing test. While my test is plain and simple, it offers a much-needed alternative to ungrounded speculation while still capturing the essence of self-awareness. I discuss how to apply the test to Large Language Models and conduct experiments on GPT-3.5 that suggest it is not self-aware. I discuss the implications of machine self-awareness and relate it to questions about meaning and understanding. Finally, I present some next steps for studying self-awareness in machines.

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

Background: The adoption of self-care behaviors among patients with arterial hypertension (AH) plays an important role in the management of their health condition. However, a lack of scales assessing self-care is observed. We aimed to develop and validate the Hippocratic hypertension self-care scale. Methods: From a pool of questions derived from a literature review, 18 items were included in the scale and reviewed by a committee of experts. Participants indicate the frequency at which they follow the self-behavior prescribed in each statement on a five-point Likert scale. Data were collected between April 2019 and December 2019. Re-sults: 202 consecutive adult patients with AH were enrolled in the study. The internal consistency of the scale was found to be 0.807, using Cronbach’s alpha coefficient. An exploratory factor analysis identified two do-mains that accounted for 92.94% of the variance of the scale items; however, each sub-scale could not be used as an independent scale. Finally, the test- retest of the scale showed a significant strong correlation (r=0.0095, p<0.001). Conclusion: Our data indicate that the scale is a reliable and valid tool for assessing self-behaviors in patients with AH. Health professionals can use it in their clinical practice to improve the management of patients’ health condition.

: BACKGROUND: The prevalence of smoking among students is a growing concern, particular-ly among medical students. Being associated with stress, competitiveness, and various social in-fluences, smoking can have detrimental effects on self-esteem and self-efficacy and may create a vicious cycle that may contribute to students’ well-being and academic performance out-comes.METHODS: A cross-sectional study on 456 medical students was conducted via an online survey using structured questionnaires on socio-demographic characteristics, smoking habits, nicotine dependence, self-esteem, and self-efficacy. RESULTS: The prevalence of smoking was 31.1% The scores of self-esteem and self-efficacy were positively correlated with smoking habits (W1=0.957, W2=0.975, P<0.05). Students presented rather low smoking addiction with male stu-dents having slightly higher scores than female students on self-esteem and self-efficacy, mili-tary students had higher self-esteem scores than civilians with physical activity having a posi-tive correlation with non-smoking behavior. CONCLUSION: According to our results, self-esteem and self-efficacy were slightly positively correlated with smoking habits. Furthermore, there was no statistically significant association between other social factors and smoking. Our results may develop a theoretical basis for medical students’ psychological variables studying for further implementation of university educational programs and preventive interventions em-powering students towards an overall better quality of life, addressing, among others, smoking behaviors.

Self-enhancement (SE) is often overlooked as a fundamental cognitive ability mediated via the Pre-Frontal Cortex (PFC). Here we present research that establishes the relationship between the PFC, SE, and the potential evolved beneficial mechanisms. Specifically, we believe there is now enough evidence to speculate that SE exists to provide significant benefits and should be considered a normal aspect of the self. Whatever the metabolic or social cost, the upside of SE is great enough that it is a core and fundamental psychological construct. Furthermore, though entirely theoretical, we suggest that a critical reason the PFC has evolved so significantly in Homo sapien is to, in part, sustain SE. We therefore elaborate as to its proximate and ultimate mechanisms.

While the desire to uncover the neural correlates of consciousness has taken numerous directions, self-face recognition has been a constant in attempts to isolate aspects of self-awareness. The neuroimaging revolution of the 1990’s bought about systematic attempts to isolate the underlying neural basis self-face recognition. These studies, including some of the first fMRI (functional Magnetic Resonance Imaging) studies, revealed a right hemisphere bias for self-face recognition in a diverse set of regions including the insula, the Dorsal Frontal Lobe, the Temporal Parietal Junction and Medial Temporal Cortex. Confirmation of these data (which are correlational) was provided by TMS (Transcranial Magnetic Stimulation) and patients in which direct inhibition or ablation of right hemisphere regions leads to a disruption or absence of self-face recognition. These data are consistent with a number of theories including a right hemisphere dominance for self-awareness and/or a right hemisphere specialization for identifying significant social relationships including to oneself.

Ever since Varela and Maturana proposed the concept of autopoiesis as the minimal requirement for life, there has been a focus on cellular systems that erect topological boundaries to separate themselves from their surrounding environment. Here we reconsider whether the existence of such a spatial boundary is strictly necessary for self-producing entities. This work presents a novel computational model of a minimal autopoietic system inspired by dendrites and molecular dynamics simulations in three-dimensional space. A series of simulation experiments where the metabolic pathways of a particular autocatalytic set are successively inhibited until autocatalytic entities that could be considered autopoietic are produced. These entities maintain their distinctness in an environment containing multiple identical instances of the entities without the existence of a topological boundary. This gives rise to the concept of a metabolic boundary which manifests as emergent self-selection criteria for the processes of self-production without any need for unique identifiers. However, the adoption of such a boundary comes at a cost, as these autopoietic entities are less suited to their simulated environment than their autocatalytic counterparts. Finally, this work showcases a generalized metabolism-centered approach to the study of autopoiesis that can be applied to both physical and abstract systems alike.

We report the development of an extrinsic self-healing coating system that shows no fluorescence from the intact coating, yellowish fluorescence in cracked regions, and greenish fluorescence in healed regions, thus allowing the separate monitoring of cracking and healing of coatings. This fluorescence monitoring self-healing system consisted of a top coating, an epoxy matrix resin containing mixed dye-loaded in single microcapsule. The dye-loaded microcapsules consisted of a poly(urea-formaldehyde) shell encapsulating a healing agent containing MAT-PDMS and styrene, a photo-initiator and a mixture of two dyes, one that fluoresces only in the solid state (DCM) and a second that fluoresces dramatically increased in the solid than solution state (4-TPAE). A mixture of the healing agent, photo-initiator and the two dyes was yellow due to fluorescence from DCM. On UV curing of this mixture, however, the color changed from yellow to green and the fluorescence intensity increased due to fluorescence from 4-TPAE in the solid state. When a self-healing coating embedded with microcapsules containing the DCM/4-TPAE dye mixture was scratched, the damaged region exhibited a yellowish color that changed to green after healing. Thus, the self-healing system reported here allows the separate monitoring of cracking and healing based on changes in fluorescence color.

Energy demand has exponentially increased with the increase in the world population and ur-banization. Renewable energy is clean, renewable, and environmentally friendly. Hydrogen production through Proton Exchange Membrane (PEM) electrolyzer is an efficient way to produce renewable energy. The efficiency and energy consumption of the PEM electrolyzer depends on the feed water quality. In this report, we study the effect of pH, TDS, and conductivity on hydrogen production as well as energy consumption during the process. pH values (3, 7, 9), TDS values (300ppm, 600ppm, 900ppm), and conductivity values (30mS/cm, 70mS/cm, 100mS/cm) were studied to understand and optimize the hydrogen production using PEM electrolyzer. The in-vestigations showed that hydrogen production is significantly affected by pH, total dissolved solids, and conductivity, and the best levels of each variable were identified by extensive testing. The findings show both the scientific significance of proton exchange electrolysis in meeting the growing energy demands of society as well as the importance of knowing the influence that di-verse conditions have on the creation of hydrogen. Our research, in general, contributes to the expanding body of scientific knowledge on the topic of the effective production of hydrogen and offers useful insights for the continuation of investigation in this field.

The efficiency of producing embryos using in vitro technologies in livestock species rarely exceeds the 30 to 40% threshold, indicating that the proportion of oocytes that fail to develop after in vitro fertilization and culture is considerably large. Considering that the intrinsic quality of the oocyte is one of the main factors affecting blastocyst yield, the precise identification of non-invasive cellular or molecular markers that predict oocyte competence is of major interest to research and practical applications. The aim of this review was to explore the current literature on different non-invasive markers associated with oocyte quality in the bovine model. Apart from some controversial findings, the presence of cycle-related structures in ovaries, a follicle size between 6 and 10 mm, large number of surrounding cumulus cells, slightly expanded investment without dark areas, large oocyte diameter (>120 microns), dark cytoplasm, and the presence of a round and smooth first polar body have been associated to better competence. In addition, the combination of oocyte and zygote selection by BCB test and spindle imaging have the potential to further optimize the identification of oocytes with better developmental competence for in vitro-derived technologies in livestock species.

Climate change effect on human-living in verities of way such as health and food security. This study presents predicting crop yields, and production risk in the near future (2020-2029) in northern Thailand using coupling 1 km resolution of regional climate model which is downscaled using a conservative remapping method and the Decision Support System for the Transfer of Agrotechnology (DSSAT) modeling system. The accuracy of the climate and agricultural model was appropriate compared to the observations with Index of Agreement (IOA) in ranges of 0.65 - 0.89. The DSSAT modeling system predicts that rice, and maize production will decrease by 5% and 4% in northern Thailand. In addition, a short-term risk analysis of rice and maize production has shown that, in the context of climate change, maize production appears to be at a high risk of low production in the near future, while rice cultivation might be a low risk.

A novel gas-phase electrocatalytic system based on a low-temperature proton exchange membrane (Sterion) was developed for the gas phase electrocatalytic conversion of CO2 to liquid fuels. This system achieved gas-phase electrocatalytic reduction of CO2 at low temperatures (below 90 ºC) over a Cu cathode by using water electrolysis-derived protons generated in-situ on an IrO2 anode. Three Cu-based cathodes with varying metal particle sizes were prepared by supporting this metal on an activated carbon at three loadings (50, 20, and 10 wt%; 50%Cu-AC, 20%Cu-AC, and 10%Cu-AC, respectively). The cathodes were characterized by N2 adsorption–desorption, temperature-programmed reduction (TPR), and X-ray diffraction (XRD) whereas their performance towards the electrocatalytic conversion of CO2 was subsequently studied. The membrane electrode assembly (MEA) containing the cathode with the largest Cu particle size (50%Cu-AC, 40 nm) showed the highest CO2 electrocatalytic activity per mole of Cu, with methyl formate being the main product. This higher electrocatalytic activity was attributed to the lower Cu–CO bonding strength over large Cu particles. Different product distributions were obtained over 20%Cu-AC and 10%Cu-AC, with acetaldehyde and methanol being the main reaction products, respectively. The CO2 consumption rate increased with the applied current and the reaction temperature.

Background: The adoption of self-care behaviors among patients with congestive heart failure (CHF) is essential for the management of their health condition. However, there is a lack of tools for estimating self-care in CHF patients. We aim to develop and validate the Hippocratic heart failure self-care scale (HHFSCS). Methods: the scale includes 22 items which are reviewed by a committee of experts. Individuals indicate the frequency at which they follow each self-behavior on a five-point Likert scale. Adult patients with CHF (n=250) from a General Hospital, in Athens, were participated in the study from June 2020 to March 2021. Reliability coefficients and Explanatory Factor Analysis (EFA), using a Varimax rotation and the Principal Components Method were used to assess the psychometric measurements. Results: The Cronbach’s alpha coefficient of the HHFSCS was 0.807. The exploratory factor analysis identified two domains that accounted for 88.44% of the variance of the scale items; however, each sub-scale could not be used as an independent scale. Finally, the test-retest showed a significant strong correlation (r=0.973, p&lt;0.001). Conclusions: The HHFSCS is a reliable and valid tool for assessing self-behaviors in CHF patients. Health professionals can use it in their clinical practice to improve the management of patient’s health conditions.

Online education allows learners to develop knowledge and skills flexibly and conveniently—such observation among students whose characteristics involve student engagement, self-regulation, and self-efficacy. However, studies to characterize Filipino online learners seem lacking. Thus, this study aimed to characterize science education tertiary students in the Philippines concerning their online student engagement (OSE), self-regulated learning (SRL), and online learning self-efficacy (OLSE). The unprecedented events brought by COVID-19 pandemic also urged the implementation of online modalities while there is no available information on students’ online learning profiles. Hence, researchers used a survey research employed through ex post facto approach to determine the effects of the demographic profile on OSE, SRL, and OLSE. The survey was participated by N=373 respondents who answered the questionnaire with informed consent administered via Google Forms. Results revealed that OSE indicators moderately characterized students, while SRL and OLSE indicators are true of them, substantiated by the overall mean M=3.85(SD=0.90), M=3.86(SD=0.92), and M=3.14(SD=0.73), respectively. Also, multivariate tests showed no significant effect among the independent groups (p&gt;0.05), except for gender and OLSE interaction (p&lt;0.05) so, only in OLSE was a significant difference found in gender. In conclusion, Filipino online learners are moderate across aspects of student engagement, self-regulation, and self-efficacy.

Secure attachment, developed through consistent relationships with attachment figures in childhood, is a crucial factor in fostering healthy interpersonal relationships and a positive self-perception. This research aimed to assess how perceived security, as an indicator of secure attachment, predicts adult self-concept through the mediating effects of resilience and positive self-esteem. A quantitative, cross-sectional, and non-probabilistic study was conducted with 383 participants, of whom 76.2% were females, aged between 17 and 86 years (M = 28.52, SD = 12.23). Correlational analyses revealed significant positive associations between perceived security, self-concept, resilience, and positive self-esteem. Path analysis demonstrated that resilience and positive self-esteem sequentially mediate the relationship between perceived security during childhood and adult self-concept (Total Effect R2 = .34, F(1,381) = 193, p < .001; Indirect effect of sequential mediation b = .02, CI 95% [.01, .04]). Findings suggest that the ability to cope with ad-versity and personal acceptance underlie the impact of perceived security on self-concept. It is recommended to promote not only secure attachment in family interventions but also resilience and self-esteem in programs targeting adolescents, youth, and adults.

Purpose: To identify the impact of dealing with COVID-19 patients in clinical areas on nurses' professional self-concept and self-confidence. Background: Professional self-concept is considered a critical factor in the recruitment/retention process in nursing, nursing shortage, career satisfaction, and academic achievements. Professional self-confidence is also a crucial determinant in staff satisfaction, reducing turnover, and increasing work engagement. Design: Descriptive, comparative study. Methods: The study was conducted between February to May 2021 by utilizing a convenience sampling technique. A total of 170 nurses from two facilities were recruited from two COVID-19 and non-COVID-19 designated facilities. The level of professional self-concept and self-confidence was assessed by utilizing the Nurses' Self-Concept Instrument and Self-Confidence Scale. Results: The professional self-concept level among the exposed group to COVID-19 patients was lower than the comparison group, while the professional self-confidence level among the exposed group to COVID-19 patients was similar to the comparison group. On the other hand, the satisfied staff and those who received professional training in dealing with COVID-19 patients reported a higher level of professional self-concept. Conclusions: Dealing with COVID-19 patients has an impact on professional self-concept; the exposure group was lower than those who did not deal with COVID-19 patients, while the professional self-confidence level among the exposed group was similar to the comparison group. Getting professional training in dealing with COVID-19 patients and being satisfied at work were significant factors in improving the professional self-concept. Policymakers should create strategies that target the improvement of professional training in dealing with COVID-19 patients.

Background: Level of perceived competence as a basic psychological need could trigger achievement of diabetes self-management goals. Due to lack of a specific data collection tool to measure level of self-competence among Persian speaking patients with diabetes this study was conducted for cross-cultural adaptation and psychometric assessment of the Persian version of Perceived Competence Scale for Diabetes (PCSD-P). Methods: Standard translation/back-translation procedure was carried out to prepare a preliminary draft of the PCSD-P. Content and face validity of the early draft were checked by an expert panel including 15 scholars in the field of health education and promotion as well as nursing education with experience of working and research on diabetes. The final drafted questionnaire was completed by 177 randomly selected patients with type 2 diabetes. Based on the collected data structural validity of the contrived version was appraised using exploratory and confirmatory factor analysis (EFA, CFA). Cronbach's alpha and Intraclass Correlation coefficients (ICC) were used to check the scale’s reliability and internal consistency. ; (3) Results: The estimated measures of Content Validity Index (CVI= 0.95) and Content Validity Ratio (CVR= 0.8) were in the range of acceptable recommended limits. The EFA analysis results demonstrated a single factor solution according to the items’ loadings for the component. The model fit indices i.e. RMSEA= 0.000, CFI=1, TLI=1, GFI= 0.998, NFI= 0.999 RFI= 0.995 confirmed consistency of the hypothesized one-factor solution. Values of the internal consistency and reliability coefficients were also in the vicinity of acceptable range (α= 0.892, ICC=0. 886, P= 0.001). Conclusions: The study findings revealed good internal validity and applicability of the PCSD-P to measure degree of self-competence among Persian speaking type 2 diabetes patients to manage the chronic disease. Due to unrepresentativeness of the study sample future cross-cultural test of PCSD-P on diverse and broader Persian speaking populations is recommended.

This paper addresses the development of knowledge and assessment-centered learning approaches within a reflective learning framework in a first year physics class in a university faculty. The quality of students’ reflections was scored using a Self-reporting Reflective Learning Appraisal Questionnaire at the end of each learning approach. The results showed the differences between the approaches based on reflections on the learning control through self-knowledge, by connecting experience and knowledge, as well as through self-reflection and self-regulation. Assessment-centered activities fundamentally help students identify aspects of their attitudes towards, as well as regulate, their sustainability learning education.

The objective of this work was to evaluate the roast time, weight loss and quality of coffee grain at four different roasting degrees, aiming to recognize and develop sustainable patterns to industrialize specialty coffees. Coffee (Coffea arabica L.), from eight producers, with scores between 80 and 83 from SCA classification, were used. Samples of 300 g were roasted in a cylindric rotative roaster, until the grains reached four different roasting profiles (Agtron scale 22.36, #33.64, #40.05, #59.92). Afterwards, analysis of roasting time, gas pressure, weight loss, and sensorial grades were accomplished. It was observed that samples roasted at #22.36 led to significant differences, regarding roasting time and weight loss. Furthermore, there is a significant difference at the sensorial grades of the coffee beverage. Comparing the roast degree #40.05 (standard used for specialty coffee) with the roast degree #22.36 (most used for commodities coffee), the first one has a gas consumption of 0.34 kg per coffee kg, whilst the second presented a gas consumption of 0.41 kg per coffee kg. Thus, from gas consumption, roast degree # 40.05 is the more sustainable option, along with the remaining parameters analyzed.

The objective of the study was to predict the risk of exposure to ammonia concentration in the production of broiler breeds of slow and fast-growing with low and high production density and correlate with the incidence of health injuries in broiler chickens using a learning machine. Two commercial lines of broiler chicken were used, one with fast-growing (Ross®, slaughter age 42 days) and another with slow-growing (Hubbard®, slaughter age 63 days). All slow-growing birds were housed at a density of 32 kg/m2. Fast-growing birds were housed in two different housing densities: low housing density with a final housing density of 16 kg/m2 and high density with a final housing density of 32 kg/m2. A total of 1250 birds were used in this experiment, 450 of which were fast-growing birds and 800 were slow-growing birds. In each room, 306 birds were randomly distributed in 18 boxes (6 boxes for each treatment and 17 birds per box), each with 3 nipple drinkers and a manual feeder. The dimensions of the high-density boxes were 1 x 1.3 m2 , while the low-density boxes had the same number of animals housed in a larger area, with dimensions of 2 x 1.3 m2 . The remaining 319 birds were housed randomly throughout the room and outside the pens to simulate a commercial production system condition (stock density). All birds were fed with an initial commercial feed from day 1 to day 18 of the experiment, and from day 18 until the end, each breed was fed with different feeds according to their nutritional demands. The following data analysis steps were performed: data selection, pre-processing, transformation, mining, analysis, and, interpretation of results. The classification algorithms, decision tree (J48), SMO (Sequential Minimal Optimization), Naive Bayes, and Multilayer Perceptron were applied to the training and test data sets to build a rule model for predicting ammonia risk levels in broiler chickens. The cross-validation technique was used to parameterize the analysis in all models. From the database of the first phase of analysis, with the classifier to predict the risk condition of ammonia concentration, the Spearman correlation coefficient (ρ, rho), considering the presence of pododermatitis, vision/affected and mucosal injury, which includes assessments of the trachea, bronchi, lungs, eyes, paw injuries, and other injuries. A non-parametric correlation measure was applied to injury incidence data as a function of ammonia risk level (1 and 10 ppm) to correlate injury incidence and ammonia level in the conditions studied. The best predictive model capable of evaluating and obtaining better performance was the Multilayer Perceptron when we considered greater accuracy by the level of risk of exposure to ammonia in the broiler chicken production process, including fast and slow-growing. Birds exposed to higher levels of ammonia concentration have a higher correlation coefficient when the relationship between the variables is strong. The Spearman correlation coefficient shows a stronger association between increased risks of ammonia exposure and the incidence of chicken injuries.

In the face of persistent soil degradation in Benin due to poor agricultural practices, including excessive use of chemical fertilizers, there is an urgent need to seek solutions that integrate microorganisms of interest. The aim of the study was to assess the effect of combining three strains of indigenous arbuscular mycorrhizal fungi on maize production in northern Benin. The study involved 34 growers in Ouénou, Bagou, and Kokey. The experimental set-up consisted of three elementary plots with three treatments. Growth parameters were measured every 15 days, from the 15th to the 60th day after sowing, on ten plants per plot. Plant nutritional status, grain yield and mycorrhization were measured. Results showed that biostimulant + 50% NPK_Urea had similar positive effects on growth parameters to those induced by the application of 100% NPK_Urea. Gains of 30.25 to 36.35% were recorded in plant height at Kokey. On the other hand, biostimulant+ 50% NPK_Urea induced a better phosphorus uptake of 21.08 to 27.77%. In addition, the grain yield of mycorrhizal plants was 8.37% higher than that of plants receiving 100% NPK_Urea at Ouénou. These results show that this technology could be integrated into the agricultural system to promote sustainable maize growing in Benin.

Changing climate conditions coupled with the transformations of cultivation practices and land use in sole crop-based sunflower production may significantly decline yield stability of this oilseed crop. Given that sunflower takes the third place in the world oilseed market, with 45 million tons per year, and in the fourth place in vegetable oil production, it is necessary to adapt production technologies toward sustainable agriculture. Considering that, the goal of the research was to analyze and beneficial sustainable production technology of sunflower in intercropping systems. A four-year trial was conducted in Serbia’s agroecological rain-fed conditions (45°34’23.2"N 19°86’18.9"E) using a split-plot design. Two oil types and one confectionary sunflower hybrid were intercropped with common vetch, red clover and alfalfa. Analyses showed that intercropping of sunflower with common vetch resulted in the decrease in almost all sunflower trait values. Also, sunflower × alfalfa intercropping provided to be the most appropriate. The yield of NS Gricko and Rimi PR were statistically on the same level with sole cropping, while alfalfa biomass had better results when intercropped with NS Gricko as compared to sole cropping. Concerning the general belief that yields are more stable in intercropping than in sole crop, further research in this respect is needed, in addition to the research of time and method of sowing.

Corn silage (Zea mays L.) is the most widely used energy resource in the diets of dairy cattle around the world; it stands out for its higher biomass yields, good palatability, homogeneous quality at harvest and ease of silage due to its higher soluble sugar content. It was carried out a search of studies related to dry matter yield (ton ha-1), population density (plant density ha-1), dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), non-fibrous carbohydrates (NFC), organic matter (OM), DM digestibility (DMD) and neutral detergent fiber digestibility (NDFD) and milk production per hectare (kg of milk ha-1) that was determined using MILK2006®. It was carried out a cluster analysis (CL, PROC CLUSTER) obtaining six groups of corn silage: CL1: "Starch", which included DMD, DFDN, TDN1xDM, Mega calories per kg of DM and kg of milk/ton/DM; CL2: "Fats" which included TDN1xMS, Mcal/kg/DM and kg of milk/ton/DM; CL3: "Dry Matter", conformed only by DFDN; CL4: "Plant Density" including Yield of DM ha-1 and Milk Production ha-1; CL5: "Neutral Detergent Fiber" and CL6: "Raw Protein". It is concluded that CL1 was characterized by a higher DMD, DFND, CNF and starch that allow a higher TNDx1DM and an energy concentration (Mcal/kg/DM) that shows a higher milk production (kg of milk/ton/DM ha-1). The characteristics of CL2 with higher EE, allow a higher TNDx1DM and an energy concentration (Mcal/kg/DM) with a higher milk production (kg of milk/ton/DM ha-1).

The relationship between DM yield/cutting and fermentable organic matter (FOM) content of tropical grasses was appropriately investigated to re-access optimal grass maturity to feed dairy cattle. Nine different grass species belonging to the genera: Brachiaria spp. (Mulato II, Ruzi), Panicum spp. (Guinea, Hamil, Mombasa, TD58), and Pennisetum spp. (King, Napier, VA06) were chemically analyzed and subjected to an in vitro gas production (IVGP) test. For 72 h, gas production (GP) was continuously recorded with fully automated equipment. A triphasic, nonlinear, regression procedure was applied to analyse GP profiles. Across all the grasses, it was found that the neutral detergent fiber (NDF) contents increased with increasing maturity of the grass while the CP contents decreased with increasing NDF contents. In all nine grasses, digestible organic matter (dOM) was significantly affected by the week of cutting but IVGP was similar between weeks of cutting in Ruzi, Hamil, Mombasa, and Napier grass. Except for Guinea grass, the lowest dOM values were found when the grasses were cut after ≥ 5 weeks of regrowth. Harvesting grass one or two weeks earlier than normal cutting time is a practically relevant intervention in in-creasing forage quality and productivity of dOM and fermentation potential.

Accurate prediction of crop production is essential in effectively managing agricultural countries' food security and economic resilience. This study evaluates the performance of statistical and machine learning-based methods for large-scale crop production forecasting. We predict the quarterly production of 325 crops (including fruits, vegetables, cereals, non-food, and industrial crops) across 83 provinces in the Philippines. Using a comprehensive dataset of 10,949 time series over 13 years, we demonstrate that a global forecasting approach using a state-of-the-art deep learning architecture, the transformer, significantly outperforms traditional local forecasting approaches built on statistical and baseline methods. By leveraging cross-series information, our proposed way is scalable and works well even with time series that are short, sparse, intermittent, or exhibit structural breaks/regime shifts. The results of this study further advance the field of applied forecasting in agricultural production and provide a practical and effective decision-support tool for policymakers that oversee the farm sector on a national scale.

The maximization of entropy S within a closed system is accepted as an inevitability (as the second law of thermodynamics) by statistical inference alone. The Maximum Entropy Production Principle (MEPP) states that not only S maximizes, but $\dot{S}$ as well: a system will dissipate as fast as possible. There is still no consensus on the general validity of this MEPP, even though it shows remarkable explanatory power (both qualitatively and quantitatively), and has been empirically demonstrated for many domains. In this theoretical paper I provide a generalization of entropy gradients, to show that the MEPP actually follows from the same statistical inference, as that of the 2nd law of thermodynamics. For this generalization I only use the concepts of super-statespaces and microstate-density. These concepts also allow for the abstraction of 'Self Organizing Criticality' to a bifurcating local difference in this density, and allow for a generalization of the fundamentally unresolved concepts of 'chaos' and 'order'.

The aims of this study are to measure the technical efficiency (TE) of Cambodian household’s rice production and trying to determine its main influencing factors using the stochastic frontier production function. The study utilized primary data collected from 301 rice farmers in three selected districts of Battambang by structured questionnaires. The empirical results indicated the level of household rice output varied according to differences in the efficiency of production processes. The mean TE is 0.34 which means that famers produce 34% of rice at best practice at the current level of production inputs and technology, indicates that rice output has the potential of being increased further by 66% at the same level of inputs if farmers had been technically efficient. Furthermore, between 2013-2015 TE of household’s rice production recorded -14.3% decline rate due to highly affected of drought during dry season of 2015. Moreover, evidence reveals that land, fertilizer, and pesticide are the major influencing input factors of household’s rice production, while disaster, education of household head, family size and other crops’ cultivated area are core influencing factors decreasing TE. Conversely, the main influencing factors increasing TE are irrigated area, number of plot area and sex of household head.

The aims of this study are to measure the technical efficiency (TE) of Cambodian household’s rice production and trying to determine its main influencing factors using the stochastic frontier production function. The study utilized primary data collected from 301 rice farmers in three selected districts of Battambang by structured questionnaires. The empirical results indicated the level of household rice output varied according to differences in the efficiency of the production processes. The mean TE is 0.34 which means that famers produce 34% of rice at best practice at the current level of production inputs and technology, indicates that rice output has the potential of being increased further by 66% at the same level of inputs if farmers had been technically efficient. Furthermore, between 2013-2015, TE of household’s rice production recorded -14.3% decline rate due to highly affected of drought during dry season of 2015. Moreover, evidence reveals that land, fertilizer, and pesticide are the major influencing input factors of household’s rice production, while disaster, education of household head, family size and other crops’ cultivated area are core influencing factors decreasing TE. Conversely, the main influencing factors increasing TE are irrigated area, number of plot area and sex of household head.

Today, issues related to people’s mental health and well-being have been described as a challenge for society, globally as well as in Sweden. This calls for new approaches to mental health promotion. The aim was to evaluate the adequacy of the content and structure, describing experiences of study circles as a means of supporting participants' self-care and self-compassion. The overall design is a descriptive, QUAL+quan design, where quantitative and qualitative results are integrated. Five participants participated in a focus group interview, of whom four completed questionnaires. One individual interview was conducted with the study circle leader. Study circles can be an arena for mental health promotion as learning and sharing of experience contributes to a sense of coherence, as well as self-compassion and a genuine concern for one’s own and others' wellbeing, but not considered an alternative to psychiatric care, for those in need of professional services. Study circles can be a possible means to support self-care and thereby promote mental health in the general population, and a valuable contribution to public health. However, in addition to modifications of the content, further research is needed on the qualifications for study circle leaders as well as the dissemination of study circles.

Self-compacting concrete (SCC) production is a complex operation that requires finding a good combination and a suitable dosage of each of its constituents. Several formulation methods have been developed to meet the workability requirements of SCC. In this paper, the relationships between the fresh properties of SCC based on local metakaolin (MK) and the spreading of its mortar was investigated. The results showed the existence of good correlations between the spreading of the mortar and the fresh state properties of the related SCC and that the MK substitution rate did not affect these correlations. The choice of the mortar flow rate should target appropriate fresh properties of the SCC and a viscosity enhancing agent (VEA) could be used if the mix is too fluid and present an instability.

Based on the sensitive sum frequency generation cross-correlation frequency-resolved optical gating (SFG-XFROG) measurement setup, besides the pulse broadening, blue shift, red shift and obvious pulse acceleration, we observed the soliton evolution when the low energy soliton pulse with wavelength of 1555nm transmit through the Si photonic crystal waveguide. The measurements were nicely matched with the simulation results, which are achieved with an optimized nonlinear Schrödinger equation (NLSE) modeling. The effects of various parameters of the silicon photonic crystal waveguides and the incident pulses on the pulse transmission were also analyzed, including the nonlinear effects and dispersion such as the self-phase modulation (SPM), self-steepening (SS) and intra-pulse Raman scattering(IRS). The results help us understand further the ultra-fast nonlinear dynamics of soliton in silicon-based waveguides, and even open a novel way for soliton-based functional elements in CMOS-compatible platforms.

Building on the Sustainable Happiness Model, this study examined how congruency between ideal self-image and brand image influence a sense of happiness. The findings show that when ideal self-image and ideal social self-image are congruent with brand image a sense of happiness can be enhanced through brand identification and positive emotions. This study contributes to literature as it reveals the mechanism of how congruency between ideal self-image and brand image positively affect happiness.

Purpose: This study aims to analyze the factors that affect the behavior of user-defined avatars in different virtual environments, and compare the differences in public self-awareness, self-expression, and emotional expression among customized avatars in multiple virtual contexts. Methods: Using a between-subjects experimental design, two random groups of participants were asked to customize avatars for themselves in two contexts, a multiplayer online social game (MOSG) and a virtual meeting (VM). i.e. a relaxed and a serious social environment. Results: When subjects perceived a more relaxed environment, the customized avatars had less self-similarity, and the subjects exhibited a stronger self-disclosure willingness and enhanced avatar wishful identification; nevertheless, public self-consciousness was not increased. When subjects perceived a more serious environment, the customized avatars exhibited a higher degree of self-similarity, and the subjects exhibited a greater self-presentation willingness, along with enhanced identification of avatar similarity, and increased public self-consciousness. Conclusions: Participants expressed positive emotions, suggesting that avatars play a positive role in various virtual contexts. The virtual context affects the self-similarity of user-customized avatars, and avatar self-similarity affects self-presentation and self-disclosure willingness, and these factors will affect the behavior of the user-customized avatar. This study contributes suggestions to the Metaverse avatar customization platform design.

Background: This study aimed to develop and evaluate a dietary self-care promotion program for hemodialysis patients based on Hurley's self-efficacy model. Methods: The needs of hemodialysis patients were identified, and an online community-based dietary self-care promotion program was developed with expert evaluations. 44 hemodialysis patients were randomly assigned to an experimental group (n=22) and a control group (n=22). The program was applied to the experimental group for four weeks. Before and after the intervention, as well as four weeks after the intervention, dietary self-efficacy, dietary self-care behavior, interdialytic weight gain, serum phosphorus, and serum potassium were assessed in both groups. Results: Significant differences over time and between groups were observed in dietary self-efficacy, interdialytic weight gain, serum phosphorus, and serum potassium. The interaction between time and group was also significant. While no significant differences were found over time in dietary self-care behavior, significant group differences and interaction effects between time and group were identified. Conclusions: The results indicate that the dietary self-care promotion program using an online community for hemodialysis patients is more effective in enhancing dietary self-efficacy and dietary self-care behavior compared to conventional treatment. This suggests the clinical applicability of the proposed dietary self-care promotion program.

In recent decades, considerable attention has been focused on the design and development of surfaces with defined or tunable properties for a wide range of applications and fields. To this end, self-assembled monolayers (SAMs) of organic compounds offer a unique and straightforward route of modifying and engineering the surface properties of any substrate. Thus, alkane-based self-assembled monolayers constitute one of the most extensively studied organic thin-film nanomaterials, which have found wide application in antifouling surfaces, control of wettability or cell adhesion, sensors, optical devices, corrosion protection, organic electronics, among many other applications. Some of which have led technological transfer to industry. Nevertheless, recently, aromatic-based SAMs have gained in importance as functional components, particularly in molecular electronics, bioelectronics, sensors, etc., due to their intrinsic electrical conductivity and optical properties, opening up new perspectives in these fields. However, some key issues affecting device performances still need to be lifted off to ensure their full use and access to novel functionalities such as memories, sensors, or active layers in optoelectronic devices. In this context, we will present herein recent advances of p-conjugated systems-based self-assembled monolayers (e.g., push-pull chromophores) as active layer and their applications.

Health self-tracking is an ongoing trend as software and hardware evolve, making the collection of personal data not only fun for users but also increasingly interesting for public health research. In a quantitative approach we studied German health self-trackers (N=919) for differences in their data disclosure behavior by comparing data showing and sharing behavior among peers and their willingness to donate data to research. In addition, we examined user characteristics that may positively influence willingness to make the self-tracked data available to research and propose a framework for structuring research related to self-measurement. Results show that users' willingness to disclose data as a "donation" more than doubled compared to their "sharing" behavior (willingness to donate= 4.5/10; sharing frequency= 2.09/10). Younger men (up to 34 years), who record their vital signs daily, are less concerned about privacy, regularly donate money, and share their data with third parties because they want to receive feedback, are most likely to donate data to research and are thus a promising target audience for health data donation appeals. The paper adds to qualitative accounts of self-tracking but also engages with discussions around data sharing and privacy.

The recommendations of the panel of experts on the further development of the ‘High-Tech Strategy’ of the Government of the Federal Republic of Germany designate the biologization of economic processes along with digitization as the central driver of societal change. Various fields such as the 'biologization of materials research' were then defined in the 'Bio-Agenda' in order to walk the path from biology to innovation. In view of this perspective, we describe how the use of biological materials and mechanisms can lead to resource conservation, the production of intelligent materials and new technological and medical applications. Our strategy, based on research on ‘Biointerfaces in Technology and Medicine’, aims at the development of modular biohybrid systems that could be used as 'biofactories of the future' for sustainable production processes. To achieve this goal, in a first phase already known technologies have to be further refined and integrated in order to obtain and apply compartmentalized reaction systems on different length and time scales. In a second phase, the resulting functional units will be employed to develop dissipative systems useful for biomedical applications and advanced production processes. From a technical point of view, future success in creating 'something entirely new' will depend crucially on robust and complementary research capabilities. Since the close connection of engineering and life sciences at KIT provides an excellent basis for this endeavor, we consider the above perspectives to be feasible.

Toby Walsh in “The Singularity May Never Be Near” gives six arguments to support his point of view that technological singularity may happen but that it is unlikely. In this paper, we provide analysis of each one of his arguments and arrive at similar conclusions, but with more weight given to the “likely to happen” probability.

Welcome to the world of bioethanol revolution, in response to the soaring global energy demand, the production of bioethanol has taken center stage, captivating the attention of governments, academia, and the private sector alike. Over the years, research in this field has witnessed exponential growth, paving the way for sustainable energy production. Ethanol, the front-runner in renewable energy, can be derived from a plethora of sources. In this abstract, we will explore the exciting advancements and emerging trends in the production of bioethanol, shedding light on the promising future of sustainable energy. Bioethanol production presents a cost-effective solution by utilizing the abundance of agricultural resources. These resources serve as the foundation for producing bioethanol from various biomass sources. In this context, we evaluate the raw materials used, including sucrose-based, starchy-based, and perennial grasses. By exploring these options, we can gain a comprehensive understanding of the diverse range of agricultural feedstocks that contribute to the production of bioethanol, this paper delves into the multiple processes involved in the production of bioethanol from agricultural base biomass, unveiling a range of possibilities for harnessing renewable energy. The key steps that were thoroughly examined include pretreatment, which encompasses both physical and chemical methods, hydrolysis, direct fermentation, and saccharification. These steps serve as crucial components in the overall bioethanol production process from agricultural base biomass. By understanding and optimizing each of these stages, we can unlock the full potential of sustainable energy generation. The paper goes beyond exploring the production processes and delves into the different generations of bioethanol, each with its unique components. Additionally, it provides a comprehensive overview of the composition of agricultural and forest residues, shedding light on their potential as valuable resources for bioethanol production. Moreover, it highlights the significance of the agricultural sector in Nigeria's economy, emphasizing the importance of energy for enhancing productivity and efficiency. The integration of bioethanol as an energy source has the potential to propel this sector forward, fostering sustainable development and contributing to the overall growth of the Nigerian economy. By harnessing the power of bioethanol, Nigeria can tap into its energy potential, promoting energy utilization and creating a positive impact on various industries.

This study aimed to investigate the impact of incorporating Refuse Derived Fuel (RDF) into a production brick mixture in order to mitigate difficulties encountered in the production environment. Three distinct tests were conducted to assess the preparation process. The first test served as a reference, utilizing 100% clay material without RDF. The second test involved the inclusion of 10% RDF, obtained from a waste factory, with a grain size less than 20mm. In the third test, the RDF was mixed with silica sand after undergoing hammer milling with a 2mm sieve, followed by a 10% incorporation into the mixture. The primary objective was to comprehend the behavior of these mixtures in two critical stages: extrusion and drying. Extrusion performance was evaluated by examining the shaping process from the mold, vacuum conditions within the extruder, and the cutting of wet sample bricks. Furthermore, the drying procedure was assessed in terms of shrinkage, drying sensitivity, and appearance. Comparisons were made among the three test scenarios to identify the influence of RDF on these key parameters. Preliminary results indicate that the inclusion of RDF impacted the extrusion process. The addition of RDF resulted in variations in the flow characteristics of the mixture, affecting the shape formation from the mold and the vacuum conditions within the extruder. Additionally, the cutting of wet sample bricks exhibited some differences compared to the reference test. Concerning the drying phase, the presence of RDF influenced shrinkage behavior and drying sensitivity. The mixture with RDF showed different shrinkage patterns compared to the reference, and its drying sensitivity was altered. Moreover, the appearance of the bricks after drying exhibited notable variations due to the incorporation of RDF. In conclusion, incorporating RDF in the brick mixture introduces changes in both the extrusion and drying stages. The alteration in flow characteristics and vacuum conditions during extrusion, as well as the variations in shrinkage, drying sensitivity, and appearance during drying, suggest that further investigation is necessary to optimize the incorporation of RDF for improved brick production. These findings contribute to the understanding of the challenges and opportunities associated with the utilization of RDF in brick manufacturing processes.

We consider a stochastic production planning problem with regime switching. There are k≥1 regimes corresponding to different economic cycles. The problem is to minimize the production costs and analyze the problem by the value function approach. Our main contribution is to show that the optimal production is characterized by an exact solution of an elliptic system of partial differential equations. A verification result is given for the determined solution.

Confronted by declining fossil fuels and the need to mitigate the effects of climate change, organic-based biomass is increasingly used to produce renewable energy, and this is receiving significant attention. Biohydrogen production by dark fermentation is a promising alternative to the conventional method since it is non-polluting and energy-saving. In this review paper, a bibliometric analysis was performed to understand the current status of biohydrogen production by dark fermentation. A total of 122 articles were analyzed from PubMed between 2013 and 2022 to understand the current trend in the dark fermentative production of hydrogen. The findings revealed that there had been increasing research in the domain of dark fermentation over the past decade, signifying an increased interest in biohydrogen production through dark fermentation. A significant increase in studies geared towards optimizing the processes, including pretreatment and designing of bioreactors and choosing the right types of biomass, was herein potentially identified. This study concludes that a new perspective on biohydrogen production is needed, particularly focusing on the optimization of the dark fermentative process

The annual energy matrix has been changing in the last years because of the necessity of less dependence on fossil fuels, which are running out on the planet. Therefore, a study was started to simulate the most efficient production of H2 through COMSOL Multiphysics®, as it is a gas that in the future will be essential to supply the world's energy necessities, due to its easy to get and insert in piped gas pipes. From this study, we were able to present an optimization of parameters that presents good indications of how to obtain efficient hydrogen production in an idealized reactor.

Bacteriophages have been recognized as potential biocontrol agents in the food industry. Bacteriophages have been proposed for a variety of applications within this industry including bio preservation, pathogen detection, and as an alternative treatment method to antibiotics in animal health. The potential applications of bacteriophages are widespread throughout the entire food production process and serve to enhance food quality, prevent foodborne illnesses, and enhance the efficiency of production. The ability of bacteriophages to lyse bacterial targets with high specificity and pose no threat to mammalian cells or natural microbiota is unique and relevant in terms of suitability for food safety. This review will outline potential and current applications of bacteriophages and their respective impacts on the field.

The sense of uncertainty and fragility due to the effects and magnitude of global challenges we are facing (from pandemic circumstances to climate change impacts) requires – much more than in the past – the capacity to generate a visionary and forefront design approach in the young gen-erations aiming at stimulating their reaction attitude rather than providing consolidated tools from past conditions that no longer exist or will rapidly evolve. Within this general framework, we have investigated the effectiveness and impacts of experienced-based methods of learning and innovative educational tools in architecture aimed at shaping expertise in which the environ-mental dimension and the climate-change challenge dialogues with the context's complexity in terms of socio-cultural dynamics, real potentialities and constrains, addressing their transdisci-plinary trajectories. The paper analyses 5 international pioneering teaching experiences that provide the opportunity to understand the outcomes of collaborative and experiential learning processes in which the educational activities leverage a dialogue between diverse communities (academia-citizens-policymakers-practitioners). The study outcomes show that shifting the pedagogical paradigm towards in-field-experience-based models can improve the awareness of future practitioners for climate implications of architectural design, implement their analysis and project skills while triggering processes of knowledge transfer and co-production at community level, and allow them to better address the societal and cultural issues involved within decision making.

In this paper, the elemental construct of an I-shaped fin cooling a heat generating volume is studied through the entropy production perspective. Using the constructal design method framework with entropy production minimization as an objective leads to different optimal shape ratio, different from the ones obtained using the excess of temperature. Moreover, the optimal shape ratio evolution is not necessary the same for local entropy production minimization (excess of entropy production) and global entropy production (entropy produced in the whole area). Finally, an analysis on a possible dimensionless number is introduced for the study of constructal plates.

This research paper aims to examine the relationship between CO2, temperature, area, fertilizers and rice production in Pakistan. This study used Augmented Dickey Fuller (ADF) and Phillips Perron (PP) unit root tests to check the order of integration of each variable. The cointegration analysis with ARDL bounds testing approach is used to examine the impact of climate change on rice production in Pakistan over time series data from the period 1968 to 2014. The parameter stability test of the model is also checked at the end. The results of estimation show that the important variables of the study are cointegrated demonstrating the presence of long-run association among them. Furthermore, climate change factors, e.g. CO2 and temperature have a long-run and short-run positive effect on the production of rice in Pakistan. This present work is original and it is first time empirically tested the impact of climate change on rice production in Pakistan. The annual time series data of 47 years enhances the validity of the empirical findings. The most fruitful finding of this research is that rice production in Pakistan is positively influenced by emission of carbon dioxide (CO2) at 5 percent significance level in both long-run and short-run.

This research was aimed to study the chickpea (Cicer arietinum L.) production among smallholder farmers in Damot Gale and Humbo Woredas. A multi-stage sampling technique was used in order to determine the sample respondents. By using simple random sampling techniques four sample Kebeles were selected. Cross sectional data were collected from 182 farmer households who were chickpea producers in 2016 production season. Primary data were collected from sample households using structured questionnaire. Descriptive statistics and econometric model were employed to analyze the data. To identify the production function of chickpea crops Cobbe-Douglas production function was employed. The study suggest interventions such as intensification strategies which increase yields through proper management and use of inputs, rural infrastructure improvement increases the likelihood of production of chickpea.

As the core of intelligent manufacturing, CPS has serious security issues, especially for the communication security of its terminal M2M. In this paper, blockchain technology is introduced to address such a security problem of communications between different types of machines in CPS. According to the principles of blockchain technology, we design a blockchain for secure M2M communications. As a communication system of M2M consists of public network areas, equipment areas and private areas, we design a sophisticated blockchain structure between the public area and private area. For validating our design, we take cotton spinning production as a case study to demonstrate our solution to M2M communication problems under the CPS framework.

Sustainability is the core concern of every business; The exploration of avenues to maintain sustainability while staying competitive with high level of productivity remains a vital endeavor. Production flexibility is a key area that can enhance the sustainability of manufacturing industries as it can ensure product availability, scalability, agility/fault tolerance as well as disaster recovery potentials. Technological advancements have provided avenues where companies can enhance virtually all aspects of their operations for efficiency, effectiveness, and productivity. This paper uses both quantitative and qualitative research approach to identify the capability requirements for smart and effective production management and subsequent analysis is done using Multi-Criteria Decision-Making methodology to identify and rank various industry 4.0 technologies and concepts that can provide these smart capabilities in in manufacturing industries to aid the businesses to achieve sustainability with production flexibility. The paper identifies over 12 smart capabilities and 9 Industry 4.0 Technologies which are applicable to production management. It also compares results from the analytics of historical I4.0 implementation as discussed in literatures with the current state as deduces from survey feedbacks from various manufacturing industries.

Dementia is a public health priority, in which memory impairments are prevalent and affects an individual’s daily activities. Research has tried to find ways to support memory problems of individuals with dementia. Self reference effect (SRE) has been shown to benefit memory in healthy, non-dementia group. The study aimed to investigate if SRE will improve memory performance of individuals with dementia. We used ecologically valid Ownership Procedure and the Remember/Know Paradigm to examine the Self-Reference Effect (SRE) on memory. It was hypothesised that participants living with dementia will perform differently in the recall of items that were self-referentially encoded compared to items other-referentially encoded and that the memory will be different in control participants than in those that live with dementia. The research found that self-referential processes did not have effect on memory in individuals with dementia. We suggest that impaired self-awareness may have contributed to the lack of this effect. However, we had a small sample size and these findings should be treated with caution.

Q-switched laser radiation at wavelengths of 355 nm, 532 nm, and 1064 nm from a Nd: YAG laser was used to generate plasma in laboratory air at the target surface made of compressed nano-silver particles of size 95 ± 10 nm. The emitted resonance spectra from the neutral silver at wavelengths of 327.9 nm and 338.2 nm indicate existence of self-reversal in addition to plasma self-absorption. Both lines were identified in emission spectra at different laser irradiation wavelengths with characteristic dips at the un-shifted central wavelengths. These dips are usually associated with self-reversal. Under similar conditions, plasmas at the corresponding bulk silver target were generated. The recorded emission spectra were compared to those obtained from the nano-material target. The comparisons confirm existence of self-reversal of resonance lines that emerge from plasmas produced at nano-material targets. This work suggests a method for recovery of the spectral line shapes and discusses practical examples. In addition, subsidiary calibration efforts that utilize the Balmer series Hα-line reveal that other Ag I lines at 827.35 nm and 768.7 nm are optically thin under variety of experimental conditions and are well-suited as reference lines for measurement of the laser plasma electron density.

Good health and the promotion of well-being for all is the third of the 17 Global Goals included in the 2030 Agenda for Sustainable Development. Contributing to this goal, the current study aimed to examine the relationships between one kind of athletes’ well-being, namely state organic self-talk, with personality traits, and basic psychological need satisfaction and frustration within their sport. Athletes (N = 691; mean age 21.65) from a variety of individual (n = 270) and team sports (n = 421) completed a multisection questionnaire capturing the targeted variables. Three-step hierarchical regression analyses revealed that: In step 1, all personality traits were to some extent a significant predictor of athletes’ organic, spontaneous self-talk dimensions and goal-directed self-talk functions. In step 2, need satisfaction significantly contributed to all spontaneous self-talk dimensions and goal-directed self-talk functions (except for creating functional deactivated states) over and above personality. Finally, in step 3, need frustration significantly contributed to negative spontaneous self-talk dimensions, and to all goal-directed self-talk functions (except for instruction) over and above personality and need satisfaction. Overall, our results indicate the importance of personality traits as personal antecedents, and perceptions of basic psychological need satisfaction and frustration as social-environmental antecedents, in shaping athletes’ state organic self-talk.

Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and rare survey has been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human error and to simplify management, self-management CPS (SCPS) is a wise choice. And to achieve dependable self-management, systematic solution is necessary to verify the design and to guarantee the safety of self-adaptation decision, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focus on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means; then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaption, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaption, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.

This study analyzed the impact of climate-related natural disasters (droughts, floods, storms/rainstorms) on economic and social variables. As the Africa-specific empirical literature is limited, this study used panel data from 1961–2011 on Africa. The study used a panel data regression model analysis. The results showed that climate change-related natural disasters affected Africa’s economic growth, agriculture, and poverty and caused armed conflicts. Among the disasters, droughts are the main cause of negative impact, severely affecting crops such as maize and coffee and resulting in increased urban poverty and armed conflicts. In contrast, international aid has a positive effect but the impact is insignificant compared to the negative consequences of climate-related natural disasters. Cereal food assistance has a negative crowding-out effect on cereal production. International donors should review their interventions to support Africa’s adaptative capacity to disasters. Government efficiency has reduced the number of deaths, and this is an area that supports Africa’s adaptative efforts.

Only by understanding the ability to take third-person perspective can we begin to elucidate the neural processes responsible for one’s inimitable conscious experience. The current study examined differences in hemispheric laterality during a first-person perspective (1PP) and third-person perspective (3PP) taking task, when using Transcranial Magnetic Stimulation (TMS). Participants were asked to take either the 1PP or 3PP when identifying the number of spheres in a virtual scene. During this task, single-pulse TMS was delivered to the motor cortex of both the left and right hemispheres of 10 healthy volunteers. Measures of TMS-induced motor-evoked potentials (MEPs) of the contralateral abductor pollicis brevis (APB) were employed as an indicator of lateralized cortical activation. The data suggest that the right hemisphere is more important in discriminating between 1PP and 3PP. These data add a novel method for determining perspective taking and add to the literature supporting the role of the right hemisphere in meta representation.

This study is a synthesis of 159 articles that were selected for their relevance to comprehend key aspects of the Massive Open Online Courses (MOOCs) phenomenon, from a discourse analysis perspective. Since 2011, MOOCs are expanding worldwide so that the number of subscribers outpointed 101 million at the end of 2018. This paper explores the question whether the MOOCs are the embodiment of the global one-world classroom or whether, instead, they represent a low-cost alternative tailored to a segment that doesn´t have enough time or resources to attend a brick-and-mortar college. In addition, the review tackles the link between motivation and low completion rates. Finally, we discuss the need to devise better methods to assess the pedagogical value of MOOCs.

AbstraIncorporating knowledge of indigenous forage legumes or associated nutritional attributes in cultivated pastures has the potential to improve grazing conditions, rangeland conditions, and cattle productivity. Climate change and other site-specific factors such as land sizes and other grazing management practices play a significant role in food security, job creation, and environmental health. Several research results have reported that the utilization of forage legumes in most farming systems has proved to increase herbage yield and soil fertility. The major aim of this review is to elucidate the potential benefits of intercropping indigenous forage legumes such as cowpea (Vigna unguiculacta L.Walp) and Dolichols (Lablab purpureaus) intercropped with Chloris gayana, this is to assesses the principal advantages, productivity, and overall performance of forage legumes grown in conjunction with grasses. Moreover, this review seeks to provide insights into the optimal management practices for intercropping forage legumes with grasses. The promising feature of intercropping South African pastoral systems must target the ability of forage legumes in terms of performance and utilization of agro-symbiotic relationships of N2 fixation symbiotic relationship in pastures. Moreover, this review aims also to address this knowledge gap by assessing the effects of defoliation frequency on the biomass production of selected forage legumes when intercropped with Chloris gayana in different growing seasons. By examining how varying defoliation frequencies influence biomass production, this review aimed to provide practical recommendations for optimizing forage management practices and promoting sustainable forage production systems. Through this investigation, we can gain insights into the complex dynamics between the defoliation frequency, intercropping systems, and biomass production.

Bovine Dewclaw Dermatitis (BDCD) is a hoof disease characterized by inflammation of the second and fifth accessory digits and the skin in this region. This pathology is poorly described in the literature; however, it has recently been observed in beef cattle in the Amazon Biome, Brazil. The objective of this study was to identify the epidemiological factors associated with BDCD onset in cattle in the analyzed biome. Samples were collected from eight farms with extensive breeding systems located in Xinguara, Rondon do Pará, Curionópolis, and Ipixuna do Pará, in the state of Pará, Brazil. A total of 706 Nellore and Nellore crossbred with taurine bovine of both sexes were evaluated, with males aged between 2–4 years and a mean weight of 650 kg and females aged between 2–11 years and a mean weight of 400 kg. Distal extremities were inspected during cattle management, and in case of dewclaw lesions, a specific examination was carried out after proper restraint. Bovine were diagnosed with BDCD in all farms analyzed. Of the 706 cattle inspected, 49 (6.94%) showed BDCD, of which 19 (38.77%) were Nellore and 30 (61.22%) were crossbred. This was the first study to determine BDCD’s occurrence in extensive farming systems in the Amazon re-gion, also showing that pastures with large amounts of stumps and stones, the physical structure of pens, and trauma and injury incidence during animal management are the most important pre-disposing factors for the onset of BDCD.

The low and unstable performance is still one of the major obstacles for anaerobic digestion, which provokes researchers to develop some strategies to improve it. In addition to traditional approaches such as co-digestion, pretreatment, and recirculation, some emerging strategies, namely additive and microaeration have also been recognized and developed in recent years due to the update of knowledge. Many studies have evaluated the effect of those strategies on digestion performance. However, it lacks a unified analysis and induction, especially the mechanism for different strategies. Therefore, this review presents a comprehensive view of research progress on the strategies based on latest researches considering five main strategies. Critical think and summary of their mechanism, reactor performance, and availability of those strategies were executed. The results demonstrated that the contribution of microaeration is mainly to balance the composition and activity of hydrolysis, acidogenesis, and methanogenic archaea. Recirculation and co-digestion balance mass and reaction environments. Pretreatment makes the structure of the substrate (such as removing lignin, reducing cellulose crystallinity, and increasing the substrate-specific surface area) more conducive to microbial utilization. The mechanism of the additive strategy varies greatly depending on the type of additive, such as enhancing interspecies electrons transfer through conductive materials, resisting adverse digestion conditions through functional microbial additives, and accelerating nutrient absorption by regulating the bioavailability of trace elements. Although these strategies have different mechanisms for promoting digestion performance, their ultimate goal is to make the parameters of the reactor to an ideal status, and then to achieve a balance among substance, microorganisms and water in anaerobic reactor.

ABSTRACT"In the realm of the industrial complex, a pressing concern lies in optimizing resource utilization and enhancing energy efficiency. This critical issue finds strong support from a variety of government programs, strategies, and regulatory documents. To effectively tackle this challenge and revolutionize petrochemical production, it is imperative to establish and develop innovative mechanisms. One such mechanism involves harnessing the power of automation and advanced Industry 4.0 technologies to streamline production processes. By doing so, we can unlock the full potential of these systems, driving operational efficiency, conserving valuable reserves, and propelling innovation within the petrochemical industry."The article delves into an in-depth analysis of the prominent trends in the application of the "Industry 4.0" concept within the petrochemical industry. It sheds light on the various factors that impact the efficiency of organizing production systems. Additionally, the article identifies the primary avenues that drive the advancement and enhancement of production organization practices. Moreover, the article explores the fundamental tools employed for process improvements and highlights the significance of process automation in petrochemical plants. By examining these key aspects, it aims to provide valuable insights and recommendations for leveraging Industry 4.0 technologies to optimize production processes in the petrochemical industry..The study focuses on examining and evaluating various systems and technologies that contribute to the optimization of production processes within the petrochemical industry. Specifically, it delves into the implementation and benefits of the just-in-time system, the 5S system for workplace rationalization, as well as different information systems including ERP systems and CALS-technologies. By exploring these systems and technologies, the article aims to identify the advantages of automating production processes within the petrochemical complex. This analysis will unveil the potential benefits such as improved efficiency, reduced waste, enhanced productivity, streamlined operations, and increased overall competitiveness. Through this research, we aim to shed light on the value of these automation solutions and their impact on driving the petrochemical industry towards greater success.

The aim of the study was to evaluate the impact of probiotics obtained from agro-industrial substrates fermented with lactic acid bacteria and/or yeasts on the health and changes in the microbiota of the digestive tract of guinea pigs. Eighty male guinea pigs, Kuri breed, 30 days old and 250 g live weight, were randomly selected and divided into four groups of 20 animals each. T0, Control, T1, Lactobacillus acidophilus and L. bulgaricus. T2, Saccharomyces cerevisiae and Kluyveromyces fragilis and T3. L acidophilus, L bulgariccus, S cerevisiae and K. fragilis. T1, T2 and T3 contained molasses-vinasse substrate in their base, the dose administered was 0.75 mL/animal orally. The parameters evaluated were weight gain, occurrence of diarrhea and mortality, blood profile, macroscopic lesions in the digestive tract organs and changes in the microbiota of the stomach, caecum, small and large intestine. Treatments T1, T2 and T3 improved weight gain (p<0.05) and reduced the presence of guinea pigs with diarrhea (p<0.05) and there was no mortality; hematic indices did not vary significantly (p>0.05) between treatments; animals in the control group presented a greater amount (p<0.05) of macroscopic lesions in the digestive tract organs; in the T1, T2 and T3 group there was an improvement in the natural microbiota. It is concluded that the inclusion of microbial additive in young guinea pigs improves intestinal health, consequently, improves weight gain, reduces diarrhea and deaths, and normalizes the natural microbiota of the gastrointestinal tract.

This review article conducts an in-depth analysis of the role of next-generation technologies in soilless vegetable production, highlighting their groundbreaking potential to revolutionize yield, efficiency, and sustainability. These technologies, such as AI-driven monitoring systems and precision farming methods, offer unparalleled accuracy in monitoring critical variables like nutrient concentrations and pH levels. However, the paper also addresses the multifaceted challenges that hinder the widespread adoption of these technologies. The high initial investment costs pose a significant barrier, particularly for small and medium-scale farmers, thereby risking the creation of a technological divide in the industry. Additionally, the technical complexity of these systems demands specialized expertise, potentially exacerbating knowledge gaps among farmers. Ethical considerations are scrutinized, including data privacy concerns and potential job displacement due to automation. Regulatory challenges, such as international trade regulations and policy frameworks, are discussed as they may need revision to accommodate these new technologies. The paper concludes by emphasizing that while these sustainable technologies offer transformative benefits, their potential for broad adoption is constrained by a complex interplay of financial, technical, ethical, regulatory, and social factors. Comprehensive, multi-faceted solutions are therefore essential for their ethical and equitable implementation.

BDD is a hoof disease characterized by inflammation of the second and fifth accessory digits and the skin in this region. This pathology is scarcely described in the literature; however, it has recently been observed in beef cattle in the Amazon Biome region. The objective of this study was to identify the epidemiological factors associated with BDD onset in cattle in the analyzed biome. Samples were collected from eight farms with extensive breeding systems located in Xinguara, Rondon do Pará, Curionópolis, and Ipixuna do Pará, in the state of Pará, Brazil. A total of 706 Nellore and Nellore crossbred with taurine animals of both sexes were evaluated, with males aged between 2–4 years and a mean weight of 650 kg and females aged between 2–11 years and a mean weight of 400 kg. Distal extremities were inspected during cattle management, and in case of accessory digit lesions, a specific examination was carried out after proper restraint. Animals were diagnosed with BDD in all farms analyzed. Of the 706 cattle inspected, 49 (6.94%) showed BDD, of which 19 (38.77%) were Nellore and 30 (61.22%) were crossbred. This was the first study to determine BDD’s occurrence in extensive farming systems in the Amazon region, also showing that pastures with large amounts of stumps and stones, the physical structure of pens, and trauma and injury incidence during animal management are the most important predisposing factors for the onset of BDD.

Vitamin E is a group of eight compounds divided into two subgroups: four tocopherols (α-, β-, γ-, and δ-) and four tocotrienols (α-, β-, γ-, and δ-). α-Tocopherol is considered the most biologically active form. This review paper summarizes the current knowledge and historical progression of vitamin E research, with particular emphasis on its role in animal nutrition. Early studies in the 1920s identified vitamin E’s role in preventing neonatal mortality in rats. Over the following decades, scientists discovered the compound’s chemical structure and its importance for the immune system, skin health, anti-inflammatory properties, hormonal balance, and other bodily functions. Production of vitamin E products has evolved, starting with isolating the vitamin from natural sources and advancing to synthesizing it with greater efficiency and standardized potency units. Early animal studies focused on reproductive health and growth disorders, but further research demonstrated the importance of vitamin E in preventing encephalomalacia in domestic fowl and muscular dystrophy in rabbits and other animals. Today, vitamin E is commonly used as a feed additive to meet animals’ requirements and assure immune function and overall health. Despite much knowledge on vitamin E’s role in animal nutrition, many questions remain unanswered, and current research aims to determine optimal supplementation levels, interactions with other nutrients, impacts on gene expression, cell signaling and communication, and effects of supranutritional dosages in livestock. Vitamin E has a long and fascinating history in scientific research, with significant advancements in understanding its chemical structure and biological activity. In animal nutrition, vitamin E continues to play an important role in improving animal health, and ongoing research will continue to expand our understanding of its benefits and mechanisms of action.

Exercise produces oxidants from a variety of intracellular sources, including NADPH oxidases (NOX) and mitochondria. Exercise-derived ROS are beneficial, and the amount and location of these ROS are important to avoid muscle damage associated with oxidative stress. We discuss here some of the evidence that involves ROS production associated with skeletal muscle contraction and the potential oxidative stress associated with muscle contraction. We also discuss the potential role of H2O2 produced after NOX activation in the regulation of glucose transport in skeletal muscle. Finally, we propose a model based on evidence for the role of different populations of mitochondria in skeletal muscle in the regulation of ATP production upon exercise. The sub-sarcolemmal population of mitochondria has the enzymatic and metabolic components to establish a high mitochondrial membrane potential when fissioned at rest but lacks the capacity to produce ATP; calcium entry to the mitochondria will further increase the metabolic input. Upon exercise, sub-sarcolemmal mitochondria will fuse to intermyofibrillar mitochondria and will transfer the membrane potential to them. These mitochondria are rich in ATP synthase and will subsequentially produce the ATP needed for muscle contraction in long-term exercise. These events will optimize energy use and minimize mitochondria ROS production.

The Paeonia suffruticosa, called as 'Feng Dan', has been used for thousands of years in traditional Chinese medicine. In our chemical investigation on the root bark of the plant, five new phenolic dimers, namely paeobenzofuranone A‒E (1‒5), have been characterized. Their structures were determined by spectroscopic analysis including 1D and 2D NMR, HRESIMS, UV, and IR, as well as ECD calculations. Compounds 2, 4, and 5 showed cytotoxicity against three human cancer cell lines with IC50 values ranging from 6.7 to 25.1 μM. Compounds 1 and 2 showed certain inhibitory activity on NO production. To the best of our knowledge, the benzofuranone dimers and their cytotoxicity of P. suffruticosa are reported for the first time.

Freshwater pond aquaculture is the prevailing fish culture system worldwide, especially in developing countries. Climate change outcomes and inadequate environmental practices challenge its sustainability. This study applies emergy synthesis to assess the environmental performance of freshwater pond aquaculture in Brazil, aiming to identify and propose practices towards sustainability. As a study model, nine semi-intensive lambari farms operating at three levels of management were evaluated: low (LC), moderate (MC) and high (HC) control. Results showed that the main inputs for LC were services (27-46%), feed (7-39%), and water (15-21%), while for the MC and HC farms, they were feed (35-49% and 17-48%, respectively) and services (33-39% and 26-36%, respectively). All farms required more than 60% of their emergy from purchased inputs, resulting in low emergy sustainability index (ESI = 0.1-0.5). Replacing animal protein and oil on diet composition by vegetal sources, using superficial water instead of springwater, increasing juvenile productivity, and controlling pond fertilization can lead all systems to higher efficiency and resilience, increasing sustainability.

AMF (Arbuscular Mychorhizal Fungi) are very well known due to their importance in promoting growth and developments of plants especially vegetables. These fungi can be grown easily, stored and multiplied with simple means, also the application of these fungi is generally on the layer of the soil or near the roots in the inner layers of the soils. The growth of the amf fungi is very easy and they are highly adjustable to any soil and environmental conditions. In this review our main focus is on the use of amf for production of vegetables and also the effect of amf against insects and pests. The amf is known to reduce several symptoms caused by different insect pests and also plant diseases thereby promoting healthy growth of the plants. Also use of this amf will increase the uptake of nutrient from the soils through symbiotic relationships between plants and fungi. The uptake of important minerals which are drawn from deeper layers of soils is observed with the help pf amf. This study reveals the benefits of the use of amf under severe disease and pest incidences thereby known as an alternate for harmful chemical pesticides and fungicides.

Ruminant mammals extract nutrients from plant-based food through fermentation in the rumen; fiber and starch are pre-digested by microorganisms and methane is produced as a by-product, which released into the atmosphere acts as a potent greenhouse gas. In an effort to reduce enteric methanogenesis, dietary additives for ruminants have been investigated, and marine macroalgae have proven particularly promising, e.g., the inclusion of 0.2% dry matter of the red alga A. taxiformis into cow feed decreased in vivo methane production by up to 98%. Thus, if globally applied, the addition of algae in ruminant diets could revolutionize the management of greenhouse gas emissions across the livestock sector. However, the ozone-depleting nature of halogen compounds produced in Asparagopsis sp. and the reported adverse health impacts on humans, along with impracticability issues and the difficulty to produce, commercialize and distribute algae widely, has sown some doubt on the feasibility of using macroalgae as methane mitigation instruments. To circumvent such obstacles, and taking into account the paradigm that eukaryotic hosts cannot be understood without considering interactions with their associated microbiome, the exploration of marine algae associated microorganisms is anticipated. Following the notion that in the close and intimate relationships between algae-hosts and their microbiota the origin of chemical response mechanisms is often unclear, and that compounds initially assigned to algae have previously been shown to stem from host-associated microbes, it is not unreasonable to think that these may be involved in the antimethanogenic effects of marine algae in the rumen. Once identified, such microorganisms could lead to antimethanogenic feed additives, and reduce enteric methanogenesis from livestock ruminants substantially. This review is three-fold: it provides a brief, historic overview of macroalgae as feed supplements for ruminants, sums up the difficulties related to using whole-macroalgae as large-scale antimethanogenic feed additives, and describes the macroalga microbiome, including its potential to serve as an antimethanogen for enteric fermentation.

This article presents the integration of lean 6S methodologies and hazard analysis and critical control points (HACCP) in the food production sector. Through the study, it is seen that non-food industrial production is not very different from that of food and, in many cases, it assimilates protocols and ideas that are already working in the food industry; Such is the case of risk analysis, critical control points or hygiene, which are part of the food production protocol and, increasingly, of the industry in general. After the integrative analysis, the article proposes a common lean 6S - HACCP model, which can be used both in food production and in non-food industrial production.

This work analyzes the visibility and scientific impact of publications related to agricultural value chains. The incidence of bibliometric indicators allows for the interpretation of bibliographic information generated worldwide. Objective: The objective of this research is to analyze the published literature and bibliometric indicators on agricultural value chains. The Web of Science database was used to extract value chains data. The study analyzed articles published between 2010 and 2020. The keywords used are "agricultural value chains'' and articles from journals or studies related to the subject were selected for bibliometric analysis and methodological review. In the search for the keyword, a total of 4208 results were extracted, of which 1,669 records were considered for analysis. The bibliometric analysis of the data reveals that Wageningen University (55) has the highest number of publications, followed by Chinese Acad Sci (26). The author Klerkx L (9) has the highest number of records, followed by Hellin J (7). With respect to the countries with the greatest contributions on the subject are: the People's Republic of China, Germany, Italy, France and the United States. The study contributes to the analysis of bibliometrics and provides a methodological review of published journal articles on agricultural value chains. This bibliographic study presents the history of research development in agricultural value chains.

Green electrospun materials are gaining popularity in the quest for a more sustainable environment for human life. Bee pollen (BP) is a valuable apitherapeutic product, and has many beneficial features such as, antioxidant and antibacterial properties. Alginate is a natural and low-cost polymer. Both natural materials show good compatibility with human tissues for biomedical applications and have no toxic effect on the environment. In this study, bee pollen-loaded sodium alginate and polyvinyl alcohol (SA/PVA) nanofibrous mats were fabricated by the electrospinning technique. The green electrospun nanofibrous mats were analyzed by scanning electron microscopy (SEM), Fourier transforms infrared spectroscopy (FTIR), and differential scanning calorimeter (DSC). According to the findings of the study, the toxin-free electrospinning method is suitable for producing green nanomaterial. Because of the useful properties of the bee pollen and the favorable biocompatibility of the alginate fibers, the bee pollen-loaded SA/PVA electrospun mats have the potential for use in a variety of biomedical applications

The object of this work is to report some preliminary results on the mechanical harvesting of blueberry fruits of cv. Cargo® in the Piedmont region, one of the most productive areas of Italy that is specialized for fresh blueberry production. The automatization of harvesting operations could represent a competitive advantage for the investigated area’s blueberries supply chain, but could act as a limitation to maintaining the quality of fresh berries. A prototype machine and a commercial harvester (Easy Harvester®) were compared to manual picking, considering harvest efficiency (share of loss), labor productivity and harvesting cost. In the indicated context, the cost of labor exceeds 2.00 euros per kg of sellable product. The use of the prototype allowed a 37% re-duction of this cost, and the use of the Easy Harvester® allowed a reduction of about two thirds. It should be emphasized that these positive performances do not consider two other aspects: the re-duction in the marketable volume (attributable to losses in the harvest and post-harvest phase), and the reduction in the net sale price by 0.30 euros due to the sorting/selection costs in the ware-house. In this study, we highlight how the transition to mechanical harvesting requires the trans-formation of several farming and warehouse operations, such as new crop varieties, new field configurations, and new packaging processes. However, a possible technical improvement of the Easy Harvester® machine could represent an opportunity for Italian companies in the planning of the production and marketing of berries, involving all actors of the supply chain. Further research on the use of mechanization in the sector must still be continued and supported.

The present study was aimed to evaluate the suitability of agro-wastes and crude vegetable oils for the cost effective production of poly-β-hydroxybutyrate (PHB), to evaluate growth kinetics and PHB production in Alcaligenes faecalis RZS4 and Pseudomonas sp. RZS1 with these carbon substrates and to study the biodegradation of PHB accumulated by these cultures. Alcaligenes faecalis RZS4 and Pseudomonas sp. RZS1 accumulate higher amounts of PHB corn (79.90% of dry cell mass) and rice straw (66.22% of dry cell mass) medium respectively. The kinetic model suggests that the Pseudomonas sp. RZS1 follows the Monod model more closely than A. faecalis RZS4. Both the cultures degrade their own PHB extract under the influence of PHB depolymerase. Corn waste and rice straw appear as the best and cost-effective substrates for the sustainable production of PHB from Alcaligenes faecalis RZS4 and Pseudomonas sp. RZS1. The biopolymer accumulated by these organisms is biodegradable in nature. The agro-wastes and crude vegetable oils are good and low cost sources of nutrients for the growth and production of PHN and other metabolites. Their use would lower the production cost of PHN and the low cost production will reduce the sailing price of PHB based products. This would promote the large scale commercialization and popularization of PHB as ecofriendly bioplastic/biopolymer.