Human milk fat is a concentrated source of energy and provides essential and long chain polyunsaturated fatty acids. According to previous experiments, human milk fat is partially lost during continuous enteral nutrition. However, these experiments were done over relatively short infusion times, and a complete profile of the lost fatty acids was never measured. Whether this lost happens considering longer infusion times or if some fatty acids are lost more than others remain unknown. Pooled breast milk was infused through a feeding tube by a peristaltic pump over a period of 30 minutes and 4, 12 and 24 hours at 2 ml/hour. Adsorbed fat was extracted from the tubes, and the fatty acid composition was analyzed by Gas chromatography-mass spectrometry. Total fat loss (average fatty acid loss) after 24 hours was 0.6 ± 0.1%. Short-medium chain (0.7%, p=0.15), long chain (0.6%, p=0.56) saturated (0.7%, p=0.4), monounsaturated (0.5%, p=0.15), polyunsaturated fatty (0.7%, p=0.15), linoleic (0.7%, p=0.25), and docosahexaenoic acids (0.6%, p=0.56) were not selectively adsorbed to the tube. However, very long chain fatty (0.9%, p=0.04), alpha-linolenic (1.6%, p=0.02) and arachidonic acids (1%, p=0.02) were selectively adsorbed and therefore lost in a greater proportion than other fatty acids. In all cases, the magnitude of the loss was clinically low.

This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.

The increased consumption of high fat-containing foods has been linked to the prevalence of obesity and abnormal metabolic syndromes. Rhizopus oligosporus, a fungus in the family Mucoraceae, is widely used as a starter for homemade tempeh. Although R. oligosporus can prevent the growth of other microorganisms, it grows well with lactic acid bacteria (LAB). Lactobacillus plantarum can produce β-glucosidase, which catalyzes the hydrolysis of glucoside isoflavones into aglycones (with greater bioavailability). Therefore, the development of a soybean-based functional food by the co-inoculation of R. oligosporus and L. plantarum is a promising approach to increase the bioactivity of tempeh. In this study, the ameliorative effect of L. plantarum in soy tempeh on abnormal carbohydrate metabolism in high-fat diet (HFD)-induced hyperglycemic rats was evaluated. The co-incubation of L. plantarum with R. oligosporus during soy tempeh fermentation reduced the homeostatic model assessment of insulin resistance, HbA1c, serum glucose, total cholesterol, triglyceride, free fatty acid, insulin, and low-density lipoprotein contents and significantly increased the high-density lipoprotein content in HFD rats. It also increased the LAB counts as well as the bile acid, cholesterol, triglyceride, and short-chain fatty acid contents in the feces of HFD rats. Our results suggested that the modulation of serum glucose and lipid levels by LAB occurs via alterations in the internal microbiota, leading to the inhibition of cholesterol synthesis and promotion of lipolysis. Tempeh, produced with both L. plantarum and R. oligosporus, may be a beneficial dietary supplement for individuals with abnormal carbohydrate metabolism.

The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. A spectral observer can be catalogued as an online algorithm for time-frequency analysis because is a method that can compute on the fly the Fourier transform (FT) of a signal, without having the entire signal available from the start. In this regard, this paper presents a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1) the reconstruction of a simple periodical signal, (2) the approximation of both a square and a triangular signal, (3) the edge detection in signals by using the Fourier coefficients, (4) the fitting of the historical Bitcoin market data from 2014-12-01 to 2018-01-08 and (5) the estimation of a input force acting upon a Duffing oscillator. To round out this paper, we present a detailed discussion about the results of the applications as well as a comparative analysis of the proposed spectral observer vis-à-vis the Short Time Fourier Transform (STFT), which is a well-known method for time-frequency analysis.

In recent years, Blockchain has been favorably adopted in the Supply Chain industry as it provides guaranteed transparency and traceability. The flexibility of Blockchain allows different applications to enable to exchange information; a significant middleweight layer is responsible for information transfer in the Agricultural Sector. The products that are manufactured at a global level in the agriculture industry are improved in safety, validation of some criteria, and quality. In the agriculture industry, the increasing number of complications associated with food safety and impurity risks needs high-level effective traceability solutions that act as necessary quality management tools to make sure satisfactory safety of crops. The agricultural supply chain today has complex ecosystems, consisting of several stockholders to authenticate criteria which are important like crop development stages, monitoring and validation, and compliance with the quality standard. In this proposed research, a systematic literature review will be done that includes smart contracts, Blockchain, and business transactions exclusively for crop production traceability across the agricultural and food supply chain. By using Blockchain in the agriculture sector, productivity, consistency, safety, reliability, and advanced security are increased. All transactions are kept and recorded on the immutable record of Blockchain associates with a decentralized system. Thus, it provides more traceability and clarity in the agriculture system in asafe, trustworthy, and effective way. A systematic literature review is thus enforced to classify the papers which are selected by the following classification: crop traceability, contribution type, research type, and their approach. For this systematic review, the papers which extracted are classified according to defined criteria. The purpose of this study is to fill the gap by collecting and analyzing studies available within the literature aiming to firstly, gain complete insight on the integration of Blockchain in the agriculture sector. Secondly, provide a summary of the present state of research on this area and identify gaps in existing studies. To achieve this aim an SLR was conducted. The findings of this SLR are discussed and researchers were provided with suggestions on possible directions for future research.

The present work aims to characterize the mechanical behavior of a new composite material for the conservation and development of the vast historical and architectural heritage that is particularly vulnerable to environmental and seismic actions. The new composite consists of natural hydraulic lime (NHL) -based mortar, reinforced by sisal short fibers randomly oriented in the mortar matrix. The NHL-based mortar ensures the chemical-physical compatibility with the original feature of the historical masonry structures (mostly in stone and clay) aiming to pursue both the effectiveness and durability of the intervention. The use of vegetable fibers (i.e. the sisal one) is an exciting challenge for the construction industry since they require a lower degree of industrialization for their processing, and therefore, their costs are also low, as compared to the most common synthetic/metal fibers. Beams of sisal-composite sizing 160x40x40 mm3 with a central notch are tested in three-point bending, aiming to evaluate both their bending strength and fracture energy. Also, tensile tests and compressive tests were performed on the composite samples, while water retention test and slump test were performed on the fresh mix. Finally, the tensile tests on the Sisal strand were carried out to evaluate the tensile strength of both strand and wire. A final comparison with unreinforced mortar specimens shows that the proposed composite ensures great workability and good performances in term of ductility and strength and it can be considered a promising alternative to the classic fiber-reinforcing systems.

Because this issue journal is dedicated to Gelatin here we present a few applications of gelatin in the field of optics. It is understood that optics is the science that studies the production, propagation, interaction and detection of light. Regarding the detection there are some materials sensitive to light (photosensitive) that are used like photomultipliers, CCD’s, crystals, two dimension (2D) materials and more. Among the 2D materials the most popular through several centuries has been gelatin based photographic emulsion that records spatial distributions of light. More recently (1970) films made of Gelatin with Dichromate (DCG) and dyes have been used. We describe some characteristics and applications of these two photosensitive materials. Also we describe examples where gelatin is used as Relative Humidity (RH) sensor and in the fabrication of optical elements based on gelatin. This article is intended to researchers outside the optics community.

Only two percent of Glioblastoma multiforme (GBM) patients respond to standard care and survive beyond 36 months (long-term survivors, LTS) while the majority survive less than 12 months (short-term survivors, STS). To understand the mechanism leading to poor survival, we analyzed publicly available datasets of 113 STS and 58 LTS. This analysis revealed 198 differentially expressed genes (DEGs) that characterize aggressive tumor growth and may be responsible for the poor prognosis. These genes belong largely to the GO-categories “epithelial to mesenchymal transition” and “response to hypoxia”. In this paper we applied upstream analysis approach which involves state-of-art promoter analysis and network analysis of the dysregulated genes potentially responsible for short survival in GBM. Binding sites for transcription factors associated with GBM pathology like NANOG, NF-κB, REST, FRA-1, PPARG and seven others were found enriched in the promoters of the dysregulated genes. We reconstructed the gene regulatory network with several positive feedback loops controlled by five master regulators – IGFBP2, VEGFA, VEGF165, PDGFA, AEBP1 and OSMR which can be proposed as biomarkers and as therapeutic targets for enhancing GBM prognosis. Critical analysis of this gene regulatory network gives insights on mechanism of gene regulation by IGFBP2 via several transcription factors including the key molecule of GBM tumor invasiveness and progression, FRA-1. All the observations are validated in independent cohorts and their impact on overall survival is studied.

Body acceleration due the heartbeat-induced reaction forces can be measured as smartphone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (SDNN and RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional ECG. Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 sec) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

Extensive limb lengthening (ELL) was completed in 75 patients: 66 achondroplasia and 9 hypochondroplasia. The average lengthening was 27cm for achondroplasia (12-40cm) and 17cm for hypochondroplasia (range 10-25cm). There were 48 females and 27 males. Lengthening was done either by 2-segment (14 patients; both tibias and/or both femurs) or by serial 4-segment lengthenings (64 patients; both femurs and tibias same time). Most patients also had bilateral humeral lengthening. Lengthenings were either juvenile-onset (31), adolescent-onset (38) or adult-onset (6). The average age at final follow-up was 26 years old (range 17-43 years). There were few permanent sequelae of complications. The most serious was one paraparesis. All patients returned to activities of normal living and only one was made worse by the surgery (paraparesis). This is the first study to show that ELL can lead to increase of height into the normal height range. Previous studies showed mean increases of height of up to 20cm, while this study consistently showed an average increase of 30 cm (range 15-40cm) for juvenile-onset and increase of 26cm (range 15-30cm) for adolescent-onset. This results in lower normal height at skeletal maturity for males and females. The adult-onset had a mean increase of 16.8 (range 12-22cm). This long-term follow-up study shows ELL can be done safely even with large lengthenings and that 4-segment lengthening may offer advantages over 2-segment lengthening. While the majority of cases were performed using external fixation, implantable limb lengthening promises to be an excellent alternative and perhaps an improvement.

Polyphosphate (polyP) accumulating organisms (PAOs) are the key agent to perform enhanced biological phosphorus removal (EBPR) activity, and intracellular polyP plays a key role in this process. Potential associations between EBPR performance and the polyP structure have been suggested, but are yet to be extensively investigated, mainly due to the lack of established methods for polyP characterization in the EBPR system. In this study, we explored and demonstrated that single-cell Raman spectroscopy (SCRS) can be employed for characterizing intracellular polyPs of PAOs in complex environmental samples such as EBPR systems. The results, for the first time, revealed distinct distribution patterns of polyP length (as Raman peak position) in PAOs in lab-scale EBPR reactors that were dominated with different PAO types, as well as among different full-scale EBPR systems with varying configurations. Furthermore, SCRS revealed distinctive polyP composition/features among PAO phenotypic sub-groups, which are likely associated with phylogenetic and/or phenotypic diversity in EBPR communities, highlighting the possible resolving power of SCRS at the microdiversity level. To validate the observed polyP length variations via SCRS, we also performed and compared bulk polyP length characteristics in EBPR biomass using conventional polyacrylamide gel electrophoresis (PAGE) and solution ³¹P nuclear magnetic resonance (³¹P-NMR) methods. The results are consistent with the SCRS findings and confirmed the variations in the polyP lengths among different EBPR systems. Compared to conventional methods, SCRS exhibited advantages as compared to conventional methods, including the ability to characterize in situ the intracellular polyPs at subcellular resolution in a label-free and non-destructive way, and the capability to capture subtle and detailed biochemical fingerprints of cells for phenotypic classification. SCRS also has recognized limitations in comparison with ³¹P-NMR and PAGE, such as the inability to quantitatively detect the average polyP chain length and its distribution. The results provided initial evidence for the potential of SCRS-enabled polyP characterization as an alternative and complementary microbial community phenotyping method to facilitate the phenotype-function (performance) relationship deduction in EBPR systems.

LiFePO₄ (LFP) or Lithium-ion battery with its advantages compared to common current motorcycle battery is an appropriate alternative in substituting wet and dry cell battery. Huge amount of demand of motorcycle along with the battery in Indonesia also make it an interesting product for business. In order to assess the commercial potential for such a new technology, market share needs to be estimated as well as the techno-economic feasibility. Hence, market share prediction using the residents of Surakarta Region and techno-economic analysis using NPV, IRR and PBP indicators have been conducted in this study. Calculation using markov chain method shows that LFP battery tends to dominate the market after certain period. Techno-economic analysis also figures out that the commercialization is feasible in three conditions - first mover, even with market leader and equilibrium point. Therefore, there is a great commercial potential for LFP battery especially in Indonesia.

Detection of early stage corrosion on slender steel members is crucial for preventing buckling failures of steel structures. An active photoacoustic fiber optic sensors (FOS) system has been reported for early stage steel corrosion detection of steel plates and rebars using surface ultrasonic waves. The objective of this paper is to investigate the surface corrosion/rust detection problem on steel rods using numerically simulated surface ultrasonic waves. The finite element method (FEM) is applied in simulating the propagation of ultrasonic waves on steel rod models. Transmission mode of damage detection is adopted, in which one source (transmitter) and one sensor (receiver) are considered. In this research, radial displacements at the receiver were simulated and analyzed by short-time Fourier transform (STFT) for detecting, locating, and quantifying a surface rust located between the transmitter and the receiver. From our time domain and frequency domain analyses, it is found that the presence, location, and dimensions (length, width, and depth) of surface rust can be estimated by ultrasonic waves propagating through the surface rust.

Climate change is expected to increase the frequency and severity of abiotic stresses that lead to loss of crop yield. We investigated the effect of salinity (S), short episodes of high temperature (HS) and combination of S+HS at the reproductive phase on dead pericarps properties and yield of the crop plant Brassica juncea. Three intervals of HS resulted in massive seed abortion; seeds from salt-treated plants germinated poorly. Pericarp extracts of salt-treated plants reduced seed germination of B. juncea; all pericarp extracts completely inhibited seed germination of tomato and Arabidopsis; removal of pericarp extracts restored seed germination. HS reduced all metabolites accumulated in dead pericarps, except for upregulation of isomaltose and cellobiose. Salt induced alteration in metabolite levels including increase in proline, reduction in TCA intermediates and changes in phytohormone levels. Proteome analysis revealed hundreds of proteins stored in dead pericarps whose levels and composition were altered under salt stress. The integration of metabolic and proteomic data showed that changes in metabolites were highly correlated with changes in proteins involved in their biosynthetic pathways. Thus, besides providing a physical shield for seed/embryo protection dead pericarps store beneficial substances whose levels, composition and biological function are altered under stress, further highlighting the elaborated function of dead organs enclosing embryos in seed biology and ecology. The detrimental effect of HS on crop production might have implications for global food security in the face of climate change.

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.

According to current guidelines, growth hormone (GH) therapy is strongly recommended in children and adolescents with GH deficiency (GHD) in order to accelerate growth rate and attain normal adult height. The diagnosis of GHD requires demonstration of decreased GH secretion in stimulation tests, below the established threshold value. Currently, GHD in children is classified as secondary insulin-like growth factor-1 (IGF-1) deficiency. Most of children diagnosed with isolated GHD presents with normal GH secretion at the attainment of near-final height or even in mid-puberty. The most important clinical problems, related to the diagnosis of isolated GHD in children and to optimal duration of rhGH therapy include: arbitrary definition of subnormal GH peak in stimulation tests, disregarding factors influencing GH secretion, insufficient diagnostic accuracy and poor reproducibility of GH stimulation tests, discrepancies between spontaneous and stimulated GH secretion, clinical entity of neurosecretory dysfunction, discrepancies between IGF-1 concentrations and results of GH stimulation tests, significance of IGF-1 deficiency for the diagnosis of GHD, a need for validation IGF-1 reference ranges. Many of these issues have remained unresolved for 25 years or even longer. It seems that finding solutions to them should optimize diagnostics and therapy of children with short stature.

Students are regarded as frequent users of mobile phones which has proven to be a convenient and acceptable method to promote healthy lifestyle. Students usually engage in relatively high levels of risky behavior and make unhealthy lifestyle choices, a study that investigates how health students access health information is necessary. The study adopted a descriptive cross-sectional study which was undertaken among third-year nursing students from three nurses training institutions in Ghana. A total of 270 students participated in the study. Most of the respondents who were currently subscribers of the health messages reported that they usually received health information on reproductive health issues, nutrition, and practicing safe sex. Most of the health students revealed that they needed more information on safe sex, diet, managing weight, and stress management. The results also show that health students are likely to remember and share short messages with friends. The findings serve as an ‘eye-opener’ for health educators and mobile service providers concerning factors that should be taken into consideration when framing health text messages that will attract health students.

Insufficient mixing in large-scale bioreactors, provokes gradient zones of substrate, dissolved oxygen, pH and other parameters. E. coli responds to a high glucose, low oxygen feeding zone with the accumulation of mixed acid fermentation products, especially formate, but also with the synthesis of non-canonical amino acids, such as norvaline, norleucine and -methyl-norleucine. These amino acids can be mis-incorporated into recombinant products, which causes a problem for pharmaceutical production whose solution is not trivial. While these effects can also be observed in scale down bioreactor systems, these are challenging to operate. Especially the high-throughput screening of clone libraries is not easy, as fed-batch cultivations would need to be controlled via repeated glucose pulses with simultaneous oxygen limitation, as has been demonstrated in well controlled robotic systems. Here we show that not only glucose pulses in combination with oxygen limitation can provoke the synthesis of these non-canonical branched-chain amino acids, but also that pyruvate pulses produce the same effect. Therefore we combined the enzyme based glucose delivery method Enbase® in a PALL24 mini-bioreactor system and combined repeated pyruvate pulses with simultaneous reduction of the aeration rate. These cultivation conditions, produced an increase in the non-canonical branched chain amino acids norvaline and norleucine in both the intracellular soluble protein and inclusion body fractions with mini-proinsulin as an example product, and this effect was verified in a 15 L stirred tank bioreactor. To our opinion this cultivation strategy is easy to apply for the screening of strain libraries under standard laboratory conditions if no complex robotic and well controlled parallel cultivation devices are available.

The aim of the studies was to test the hydraulic pressure losses along the length, local pressure losses, absolute and relative roughness values of the pipeline walls made of modern polymer material. The article presents the results of the experimental studies of hydraulic resistance of pipelines from modern polymer materials, the method of theoretical determination of resistance coefficients in the VALTEC ISO 21003 metal polymer pipeline, the classification and method of determining the values of local hydraulic resistance coefficients of T-Junctions are given, the dependence for determining the Darcy λ hydraulic friction coefficient associated with the value of the equivalent hydraulic roughness coefficient of the inner surface of pipes and the Reynolds number. The experimental data of the hydraulic coefficient of friction Darcy λ was compared with the known theoretical relationships of A.D. Altschul, Colbrooke for smooth pipes, Colbrooke and White, an alternative to the Colbrooke-White, Offengenden equation for low-shear pipes and pipes from polymer materials with Reynolds numbers Re>10^4. Dependence of hydraulic coefficient of friction on relative equivalent roughness of pipeline walls and Reynolds number, specific energy loss per one linear meter on total flow rate in system of hydraulically short pipeline is obtained. The empirical dependence of values of coefficients of local resistance ζ=f(Q2/Q3) from a ratio of the divided expenses of Q2/Q3 of the equal-pass T-Junction α=90 the pipeline to a branch up, on pass in the direct T-Junction and division of streams at various internal pipeline diameters of VALTEC.

This paper investigates the contribution of business model innovations in improvement of food supply chains. Through a systematic literature review, the notable business model innovations in the food industry are identified, surveyed, and evaluated. Findings reveal that the innovations in value proposition, value creation processes, and value delivery processes of business models are the successful strategies proposed in food industry. It is further disclosed that rural female entrepreneurs, social movements, and also urban conditions are the most important driving forces inducing the farmers to reconsider their business models. In addition, the new technologies and environmental factors are the secondary contributors in business model innovation for the food processors. It is concluded that digitalization has disruptively changed the food distributors models. E-commerce models and internet of things are reported as the essential factors imposing the retailers to innovate their business models. Furthermore, the consumption demand and the product quality are two main factors affecting the business models of all the firms operating in the food supply chain regardless of their positions in the chain. The findings of the current study provide an insight into the food industry to design a sustainable business model to bridge the gap between food supply and food demand.

We study a conformation of an albumin protein in the temperature range of 300K-315K, i.e. in the physiological range of temperature. Using simulations we calculate values of two backbone angles, that carry most of information about positioning of the protein chain in a conformational space. Given these, we calculate energy components of such protein. Further, using the Flory theory we determine the temperature in which investigated albumin chain model is closest to the free joined chain model. Near the Flory temperature, we study energy components and the conformational entropy, both derived from two angles that reflect most of the chain dynamics in a conformational space. We show that the conformational entropy is an oscillating function of time in considered range of temperature. Our finding is that, the only regular oscillation pattern appears near the Flory temperature.

Runtime verification (RV) is a lightweight approach to detecting temporal errors of system at runtime. It confines the verification on observed trajectory which avoids state explosion problem.To predict the future violation, some work proposed the predictive RV which uses the information from models or static analysis. But for software whose models and codes cannot be obtained, or systems running under uncertain environment, these predictive methods cannot take effect. Meanwhile, RV in general takes multi-valued logic as the specification languages, for example the " true, false and inconclusive" in three-valued semantics. They cannot give accurate quantitative description of correctness when "inconclusive" is encountered. We in this paper present a RV method which learns probabilistic model of system and environment from history traces and then generates probabilistic runtime monitor to quantitatively predict the satisfaction of temporal property at each runtime state. In this approach, Hidden Markov Model (HMM) is firstly learned and then transformed to Discrete Time Markov Chain (DTMC). To construct incremental monitor, the monitored LTL property is translated into Deterministic Rabin Automaton (DRA). The final probabilistic monitor is obtained by generating the product of DTMC and DRA, and computing the probabilities for each state. With such method, one can give early warning once the probability of correctness is lower than pre-defined threshold, and have the chance to do adjustment in advance. The method has been implemented and experimented on real UAS (Unmanned Aerial Vehicle) simulation platform.

The bivalve mollusk, Anadara tuberculosa (Black Shell, in Spanish Concha prieta, "CP"), is found on America's Pacific coast, where it is harvested for subsistence and commercial markets. This paper aims to diagnose the performance of the black shell's value chain. We also identify several challenges that must be addressed to improve the sustainability of the black shell fishery in Ecuador. The descriptive methodology was quantitative, with a non-experimental, field, cross-sectional, and ex post facto design. Similar questionnaires were designed, validated, and applied to each link in the production chain to collect information. The performance of the value chain was diagnosed in the dimensions of productivity, competitiveness, and quality of life, reaching a 75.1% index, a result that indicates that the value chain has a moderate to good performance level. However, the value chain can be improved if the following challenges are met: 1) Restoration of the mangrove ecosystem, 2) Promotion of low-intensity shellfish aquaculture, 3) Good sanitary management and purification, 4) Promotion of value-focused ventures, 5) Strengthening of organizations and agreements for the use and custody of mangroves, and 6) Strengthening of institutions that contribute to the advancement of these challenges.

We find that the overnight returns of Korean exchange-traded index funds (ETFs) are significantly positive, whereas the subsequent intraday returns are negative. These intraday return reversals are caused by relatively higher opening prices than the closing prices. In the Korean ETF market, where institutional investors are dominant participants, the return reversals are not explained by the attention hypothesis as in Berkman et al. [1]. Hence, we investigate whether the disagreement hypothesis can explain return reversals. Under the disagreement situations between positive and negative traders at the open, positive traders can have a positive influence on the ETF prices by increasing their investments. However, negative traders, who give up investments due to limited short selling opportunities in the ETF market, have no effects on the prices. Comparing ETF markets with KOSPI 200 Futures where there are no restrictions on short selling, we find that short selling constraints are significant factors for the return reversals. This implies that disagreement among the investors can cause return reversals even in the markets without noise traders. Using unique Korean market data, we conclude that return reversals cannot be completely explained by the attention hypothesis, and that disagreement among investors is also a significant factor for the return reversals. This study contributes to the existing literature by showing that the attention hypothesis does not explain return reversals in the ETF market completely, and suggesting the disagreement hypothesis as an alternative.

Processing of the RNA polymerase I pre-rRNA transcript into the mature 18S, 5.8S, and 25S rRNAs requires removing the “spacer” sequences. The canonical pathway for the removal of the ITS1 spacer, located between 18S and 5.8S rRNAs in the primary transcript, involves cleavages at the 3’ end of 18S rRNA and at two sites inside ITS1. The process generates a long and a short 5.8S rRNA that differ in the number of ITS1 nucleotides retained at the 5.8S 5’ end. Here we document a novel pathway that generates the long 5.8S for ITS1 while bypassing cleavage within ITS1. It entails a single endonuclease cut at the 3’-end of 18S rRNA followed by exonuclease Xrn1 degradation of ITS1. Mutations in RNase MRP increase the accumulation of long relative to short 5.8S rRNA; traditionally this is attributed to a decreased rate of RNase MRP cleavage at its target in ITS1, called A3. In contrast, we report here that the MRP induced switch between long and short 5.8S rRNA formation occurs even when the A3 site is deleted. Based on this and our published data, we propose that the switch may depend on RNase MRP processing RNA molecules other than pre-rRNA.

Long-chain branched metallocene-catalyzed high-density polyethylenes (LCB-mHDPE) were solution blended to obtain blends with varying degrees of branching. A high molecular LCB-mHDPE was mixed with low molecular LCB-mHDPE are varying concentrations, whose rheological behavior is similar but whose molar mass and molar mass distribution is significantly different. Those blends were characterized rheologically to study the effects of concentration, molar mass distribution, and long-chain branching level of the low molecular LCB-mHDPE. Owing to the ultra-long relaxation times of the high molecular LCB-mHDPE, the blends started behaving clearly more long-chain branched than the base materials. The thermorheological complexity showed an apparent increase in the activation energies Ea determined from G’, G”, and especially δ. Ea(δ), which for LCB-mHDPE is a peak function, turned out to produce even more pronounced peaks than observed for regular LCB-mPE and also LCB-mPE with broader molar mass distribution. Thus, it is possible to estimate the molar mass distribution from the details of the thermorheological complexity.

One of the most remarkable features in the 20th century was the digitalization of technical progress, which changed the output of companies worldwide and became a defining feature of the century. The growth of information technology systems and the implementation of new technical advances, which enhance the integrity, agility and long-term organizational performance of the supply chain, can distinguish a digital supply chain from other supply chains. For example, the Internet of Things (IoT)-enabled information exchange and Big Data analysis might be used to regulate the mismatch between supply and demand. In order to assess contemporary ideas and concepts in the field of data analysis in the context of supply chain management, this literary investigation has been decided. The research was conducted in the form of a comprehensive literature review. In the SLR investigation, a total of 71 papers from leading journals were used. SLR has found that data analytics integrate into supply chain management can have long-term benefits on supply chain management from the input side, i.e., improved strategic development, management and other areas.

The early diagnosis of mastitis represents an essential factor for a prompt detection of the animal for further actions. In fact, if not culled, infected cows must be segregated from the milking herd and milked last, or milked with separate milking units. Besides microbiological analysis, the somatic cell count (SCC) commonly used as predictor of intramammary infection, frequently lead to a misclassification of milk samples. To overcome these limitations, more specific biomarkers are continuously evaluated. Total amino acid content increases significantly in mastitic milk compared to normal one. Bovine mastitis can arise as a result of infection of the mammary gland by Staphylococcus aureus. Multiplication of this bacterium within the mammary gland is required for infection to persist. S. aureus requires branched-chain amino acids (BCAAs: isoleucine, leucine, valine) for protein synthesis, branched-chain fatty acids synthesis and environmental adaptation by responding to their availability via transcriptional regulators. The importance of BCAAs for S. aureus physiology necessitates that it either synthesize them or scavenge them from the environment. Increase of BCAAs in composite milk has been postulated to be linked to mammary infection by S. aureus. In the present work, we demonstrated, by a direct ion-pairing reversed-phase method based on the use of the evaporative light-scattering detector (IP-RP-HPLC-ELSD), applied to 65 composite cow milk samples, a correlation between the concentration of isoleucine and leucine and S. aureus load.

Twenty-first century infrastructure needs to respond to changing demographics, becoming climate neutral, resilient, and economically affordable, while remaining a driver for development and shared prosperity. However, the infrastructure sector remains one of the least innovative and digitalized, plagued by delays, cost overruns, and benefit shortfalls [1-4]. The root cause is the prevailing fragmentation of the infrastructure value chain [5]. To support overcoming the shortcomings, an integration of the value chain is needed. This could be achieved through a use-cased-based creation of federated digital platforms applied to infrastructure projects. Such digital platforms enable full-lifecycle participation and responsible governance guided by a shared infrastructure vision.

Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. The purpose of the present study is to review the state-of-the-art ML applications in the biofuels' life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

Critical care of neonates involves substantial usage of antibiotics and exposure to multidrug resistant (MDR) nosocomial pathogens. These pathogens are often exposed to sub-MIC doses of antibiotics which might result in a range of physiological effects. Therefore, to understand the outcome of sub-inhibitory dosage of antibiotics on Staphylococcus populations, nasal swab specimens were collected from 34 neonates admitted to the Sick Newborn Care Unit between 2017-2018, a total of 41 non-repetitive isolates were included in this study. Staphylococcus haemolyticus was the prevalent species (58.54%) with high non-susceptibility to cefotaxime (CTX) (79.16%), gentamicin (87.50%), and meropenem (54.17%). Biofilm forming abilities of S. haemolyticus isolates in the presence of sub-optimal CTX (30μg/mL), the predominantly prescribed β-lactam antibiotic, were then determined by crystal violet assays and extracellular DNA (eDNA) quantitation. CTX was found to significantly enhance biofilm production among the non-susceptible isolates (p-valueWilcoxin test- 0.000008) with increase in eDNA levels (p-valueWilcoxin test- 0.000004). Additionally, no changes in non-susceptibility were observed among populations of two MDR isolates, JNM56C1 and JNM60C2 after >500 generations of growth in the absence of antibiotic selection in vitro. These findings demonstrate that sub-MIC concentration of CTX induces biofilm formation and short-term non-exposure to antibiotics does not alter non-susceptibility among S. haemolyticus isolates.

The present article proposes a complete framework for Supply Chain Strategy (SCS) analysis that is adapted to the specific characteristics of the agrifood chain, thereby facilitating management of the former. As a specific case of analysis, the horticultural supply chain, originating in Spain and ending with the European consumer, is analyzed, taking as a reference the marketing companies at origin (mainly social economy companies, i.e.; cooperatives). In addition, a survey of marketing companies is conducted to possibly determine which explicit cooperative growth strategies may include horizontal and vertical collaboration relationships with other members of the chain. The aim is to analyze with whom the cooperative collaborates within the supply chain and the key points of such collaboration. A model analyzing the influence of collaboration on company performance is also considered. The results reveal that, in recent years, aspects related to quality and health have been surpassed and replaced by the concept of sustainability within a framework of collaboration with customers. In any case, it becomes evident that there is a need to expand collaboration within the chain by incorporating the supplier of the supplier, with the aim of making the chain more profitable.

Though innovations for sustainable management of natural resources have emerged over time, the rising demand for nature based health solutions and integration of endemic flora into global value chains could have adverse impacts on ecosystems. The ecological risks in the exploitation of wild medicinal plant resources are exacerbated by a myriad of agrotechnological risks and challenges that highly constrain their domestication. Successful exploitation and commercialization of medicinal plants thus require a clear understanding of their demand and production systems or value chain analysis. Accordingly there is need for innovative approaches towards their integration into global value chains. Since quality and safety, traceability, certification, as well as, consumer tastes and preferences are critical drivers in purchasing decisions by global consumers, they are inadvertently exploited to weaken Indigenous knowledge (IK), undermine common property rights and entrench value chains that favour a few elite buyers. This tend to create pervasive incentives for overexploitation of medicinal plant resources and environmental degradation. Potential solution lies in the recognition of drivers of vulnerability to environmental degradation and the innovative use of policy bricolage, feedback loops and interactions between knowledge, power and agency on one hand, and collective action and property rights institutions on the other hand. We conceptualise a framework that can mediate a transformational agenda and enhance systematic understanding of sustainability lenses in endemic medicinal plant resources value chains. This could in turn strengthen IK, enhance collective action and promote participation of local actors with positive impact on the utilisation and integration of endemic medicinal plant resources into global value chains.

We put forward a co-axial pump(optical)-probe(X-rays) experimental concept and show performance of the optical component. A Bessel beam generator with a central 100 micrometers-diameter hole (on the optical axis) was fabricated using femtosecond (fs) laser structuring inside a silica plate. This flat-axicon optical element produces a needle-like axial intensity distribution which can be used for the optical pump pulse. The fs-X-ray free electron laser (X-FEL) beam of sub-1 micrometer diameter can be introduced through the central hole along the optical axis onto a target as a probe. Different realisations of optical pump are discussed. Such optical elements facilitate alignment of ultra-short fs-pulses in space and time and can be used in light-matter interaction experiments at extreme energy densities on the surface and in the volume of targets. Full advantage of ultra-short 10 fs X-FEL probe pulses with fs-pump(optical) opens an unexplored temporal dimension of phase transitions and the fastest laser-induced rates of material heating and quenching. A wider field of applications of fs-laser-enabled structuring of materials and design of specific optical elements for astrophotonics is presented.

Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study the wind speed and output power are related with a LSTM method. Moreover, it can be inferred that the LSTM Network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement and the root mean square is 0.49 in the mean value related to the accuracy of LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of LSTM method.

The role of γδ T cells in vertebrate immunity is still an unsolved puzzle. Species such as humans and mice display a low percentage of these T lymphocytes (i.e., “γδ low species”) with a restricted diversity of γδ T cell receptors (TR). Conversely, artiodactyl species (i.e., “γδ high species”) ac-count for a high proportion of γδ T cells with large γ and δ chain repertoires. The genomic organisation of the TR γ (TRG) and δ (TRD) loci has been determined in sheep and cattle, noting that a wide number of germline genes that encode for γ and δ chains characterise their genomes. Taking advantage from the current improved version of the genome assembly, we have investigated the genomic structure and gene content of the dromedary TRD locus, which, as in the other mammalian species, is nested within the TR alpha (TRA) genes. The most remarkable finding was the identification of a very limited number of variable germline genes (TRDV) compared to sheep and cattle, which supports our previous expression analyses for which the somatic hypermutation mechanism is able to enlarge and diversify the primary repertoire of dromedary δ chains. Furthermore, the comparison between genomic and expressed sequences reveals that D genes, up to four incorporated in a transcript, greatly contribute to the increased diversity of the dromedary δ chain antigen binding-site.

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

This paper developed a new theory for supply chain architecture, and engineering design that enables integration of the business and supply chain strategies. The architecture starts with individual supply chain participants and derives insights into the complex and abstract concept of green-field integration design. The paper presented a conceptual system for depicting the interactions between business and supply chain strategy engineering. The system examines the decisions made when engineering the business strategy, with regards to the supply chain design. The system derived with a new understanding of how strategies are integrated, and what are the implications for engineering successful strategies. The study revealed that supply chain design is not considered in great detail before architecting the business strategies. Thus, companies consequentially experience supply chain problems that are likely to be detrimental to the growth potentials. The paper also derived with the findings that proactive and pre-emptive involvement of supply chain participants in the strategy engineering process, would lead to a more robust strategic design.

Inhibition of amyloid fibril formation could benefit patients with systemic amyloidosis. In this group of diseases, deposition of amyloid fibrils derived from normally soluble proteins leads to progressive tissue damage and organ failure. Although many small molecules have been proposed as inhibitors of amyloid formation, few have been successful in clinical trials. Amyloid formation is complex and several individual steps could be targeted by small molecules. However, the exact mechanism of action for a molecule is often not known, which impedes medicinal chemistry efforts to develop more potent molecules. Furthermore, commonly used assays are prone to artifacts that must be controlled for. Here, potential mechanisms by which small molecules could inhibit aggregation of immunoglobulin light chain dimers, the precursor proteins for AL amyloidosis are studied in assays that recapitulate different aspects of amyloidogenesis in vitro. One molecule reduced unfolding-coupled proteolysis of light chains, but no molecules inhibited aggregation of light chains or disrupted pre-formed amyloid fibrils. This work demonstrates the challenges associated with drug development for amyloidosis, but also highlights the potential to combine therapies that target different aspects of amyloidogenesis.

The objective of this study is to determine the type of governance of the four main agricultural value chains in Tamaulipas, northeast of Mexico. For the preparation of this research used a qualitative design, using a questionnaire and semi-structured interviews to a sample of representatives of the four selected chains. The results showed that in all the studied networks, control and coordination capacity is limited by the influence that has a link on the rest of the chain. In all cases, was that the industrial link is who leads the chains and exert control over the rest of the links. The results showed that, when the industrial link is located close geographically to the rest of the links (chains of rice and sugar cane), the chains showed a hierarchical type, where the industrial exercised dominion over the rest of the links and captures most of the income. On the other hand, in chains where control is exercised by links that are outside the territory (chains of soybeans and sorghum), they function as captive chains, putting the rest of the chain to its influence and control.

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora Wind Farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of five-minute intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory (LSTM), GRU, autoregressive integrated moving average (ARIMA) and support vector machine (SVM), which are tuned to optimise outcome. It is observed that the hybrid deep learning model exhibits superior performance over other forecasting models to improve the accuracy of wind power forecasting, numerically, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

This study investigates optimal decisions in a green supply chain consisting of a manufacturer and a leading retailer considering the green marketing and fairness preferences of member firms. Four Stackelberg game decision models are constructed in which the manufacturer and the re-tailer engage in green marketing separately when the manufacturer has no and has fairness preferences. The impacts of fairness preferences and green marketing on the optimal decision in the green supply chain are comparatively analyzed. The study finds that member firms perform green marketing regardless of the presence or absence of fairness preferences and that such be-havior increases the wholesale price, retail price, and market demand of low-carbon products as well as the profits of member firms and the supply chain. A more interesting finding is that the profit growth of member firms and the supply chain due to the manufacturer’s green marketing is more pronounced than that due to the retailer’s green marketing. When the retailer and the manufacturer engage in conduct green marketing, the manufacturer's fairness preferences have different effects on the wholesale price, retail price, market demand, level of green marketing efforts, member enterprises and profits of supply chain. Therefore, firms should consider the impact of green marketing and fairness preferences to make pricing and performance decisions, so as to achieve efficient operation of the whole supply chain and avoid double marginal effects. Finally, the above conclusions are verified through numerical simulation, providing a reference for the decision-making of member firms in the green supply chain.

Among the thermophysical properties, the surface / interfacial tension, viscosity and density / molar volume of liquid alloys are the key properties for the modelling of microstructural evolution during solidification. Therefore, only reliable input data can yield accurate predictions preventing the error propagation in numerical simulations of solidification related processes. Due to experimental difficulties related to reactivity of metallic melts at high temperatures, the measured data are often unreliable or even lacking. The application of containerless processing techniques either leads to a significant improvement of the accuracy or makes the measurement possible at all. On the other side, accurate model predicted property values could be used to compensate the missing data; otherwise, the experimental data are useful for the validation of theoretical models. The choice of models is particularly important for the surface, transport and structural properties of liquid alloys representing the two limiting cases of mixing, i.e. ordered and phase separating alloy systems. To this aim, the thermophysical properties of the Fe-Si and Cu-Pb systems were analysed and the connections with the peculiarities of their mixing behaviours are highlighted.

Molecular dynamics simulation is currently the theoretical technique eligible to simulate a wide range of systems from soft condensed matter to biological systems. However, of the excellent results that the technique has arrogated, this approach remains computationally expensive, but with the emergence of the new supercomputing technologies bases on graphics processing units graphical processing units-based systems GPUs, the perspective has changed. The GPUs allow performing large and complex simulations at a significantly reduced time. In this work, we present recent innovations in the acceleration of molecular dynamics in GPUs to simulate non-Hamiltonian systems. In particular, we show the performance of measure-preserving geometric integrator in the canonical ensemble, that is, at constant temperature. We provide a validation and performance evaluation of the code by calculating the thermodynamic properties of a Lennard-Jones fluid. Our results are in excellent agreement with reported data reported from literature, which were calculated with CPUs. The scope and limitations for performing simulations of high-throughput MD under rigorous statistical thermodynamics in the canonical ensemble are discussed and analyzed.

Background: Wolbachia sp. is an endosymbiotic bacterium that is widespread among arthropods and filarial nematodes. It affects the fertility and maturation of its arthropod hosts, which in some cases are vectors of infectious diseases of humans and animals. Acanthocheilonema reconditum is a canine filarial nematode, which can eventually infect humans through arthropods. An outbreak of A. reconditum has been reported recently in dogs in the southwest of Colombia, specifically in Cali. In addition, Wolbachia sp. is currently used for the biological control of the mosquito Aedes egypti in cities in the north and south-west of Colombia. Aedes egypti is a vector of dengue fever, a major public health disease in tropical and subtropical areas of the world. The objectives of this study were to detect Wolbachia sp. in mosquito and flea samples collected from dogs in southwestern Colombia and to determine the co-infection of Wolbachia sp. and A. reconditum in the recent outbreak of canine filariasis in Santiago de Cali. Methods: A total of 18 mosquitoes of the genera Aedes and Culex and 18 Ctenocephalides felis fleas were collected in Santiago de Cali and nearby municipalities. DNA was extracted and 16S rRNA and ftsZ gene fragments were identified using conventional and nested polymerase chain reaction. In addition, the presence of Wolbachia sp. was analyzed in 55 cases positive for A. reconditum previously detected in the city, using the same molecular techniques. Results: From the analysis of 18 mosquitoes and 18 fleas, using molecular techniques based on the 16S rRNA and ftsZ genes, Wolbachia sp. was detected in all the arthropods analyzed. Wolbachia sp. was not detected in the samples obtained from dogs infected with A. reconditum. Conclusions: Molecular analyses were shown to be effective for the identification of Wolbachia sp. in mosquitoes and fleas but not in samples positive for A. reconditum.

It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real time through an advanced metering infrastructure (AMI) sensor, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include the autoregressive integrated moving average, radial basis function-artificial neural network, quantitative multi-model predictor plus, and long short-term memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand by applying the data on the amount of water consumption by purpose and the pipe diameter of an end-use level of the SWG demonstration plant in each demand forecasting model. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from the AMI, and the performance of each model was assessed. The Smart Water Grid Research Group installed ultrasonic-wave-type AMI sensors in the block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the residual, root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), and Pearson correlation coefficient (PCC) as indices. The water demand forecast was slightly underestimated in models that employed the assessment results based on the RMSE and NRMSE. Furthermore, the forecasting accuracy was low for the NSE due to a large number of negative values; the correlation between the observed and forecasted values of the PCC was not high, and it was difficult to forecast the peak amount of water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

Long-chain omega-3 fatty acid status during pregnancy may influence newborn anthropometry and duration of gestation. Evidence from high-quality trials from LMICs is limited. We conducted a double-blind, randomized, placebo-controlled trial among 957 pregnant women (singleton gestation, 14-20 weeks’ gestation at enrollment) in India to test the effectiveness of 400 mg/d algal docosahexaenoic acid (DHA) compared to placebo provided from enrollment through delivery. Among 3379 women who were screened, 1171 were found eligible; 957 enrolled and were randomized. The intervention was two microencapsulated algal DHA (200 X 2= 400 mg/d) or two microencapsulated soy and corn oil placebo tablets to be consumed daily from enrollment (20 weeks) through delivery. The primary outcome was newborn anthropometry (birth weight, length, head circumference). Secondary outcomes were gestational age and 1 and 5 min Appearance, Pulse, Grimace, Activity, and Respiration (APGAR) score. The groups (DHA; n=478 and placebo; n=479) were well balanced at baseline. There were 902 live births. Compliance with the intervention was similar across groups (DHA: 88.5%; placebo: 87.1%). There were no significant differences between DHA and placebo group for birth weight (2750.6 ± 421.5 vs. 2768.2 ± 436.6 g, p=0.54), length (47.3 ± 2.0 vs. 47.5 ±2.0 cm, p=0.13) or head circumference (33.7 ± 1.4 vs 33.8 ± 1.4 cm, p=0.15). The mean gestational age at delivery was similar between groups (DHA: 38.8 ± 1.7 placebo: 38.8 ± 1.7 wk, p= 0.54) as were APGAR scores at 1 and 5 min. Supplementing mothers through pregnancy with 400mg/d DHA did not impact the offspring birthweight, length or head circumference.

Objectives: This meta-analysis was designed to assess the effect of addition of a bead-beating step during DNA extraction to effectively isolate Trichuris trichura DNA for quantitative Polymerase Chain Reaction (qPCR)-based diagnosis. Abstract was reported according to PRISMA-DTA abstract checklist. Methods: Eligibility criteria: qPCR-based molecular studies comparing the inclusion of bead-beating step during the DNA extraction from stool samples with extraction without the step were included in the analysis. Information sources: Studies using real patient samples in community settings were included. PubMed and Google search engine were searched in December 2019. Risk of bias and applicability: Risk of bias and applicability were assessed using QUADAS-2 checklist. Synthesis of results: Odds ratio for individual studies were combined to estimate Random Effects Model odds ratio. Additional literature were searched to discuss biochemical nature of helminth eggs. Results:Included studies: A total of six independent sub-studies were gathered from two published original articles. Division of the two major studies into six sub-studies was indispensable due to natures of the study carried. 128 of total 192 samples (in all studies) were positive for Trichiuris trichiura when bead-beating was used during DNA extraction compared to 108/192 when bead-beating was excluded. Combined odds ratio was 1.66 (95% CI: 1.059 to 2.602). Biochemical nature of helminth eggs was discussed. Discussions: Strengths and limitations: Though only two article were included in the study, six exclusive individual sub-studies were analyzed. Inherent differences in the background prevalence of helminth in study population could impact sensitivity of qPCR. Interpretation: It was found that the inclusion of the bead-beating step during DNA extraction significantly increased the sensitivity of the test.

Pentadecanoic acid (C15:0) and heptadecanoic acid (C17:0) have been described as dietary biomarkers of dairy-fat consumption with varying degrees of reliability between studies. It remains unclear how the total amount of dietary fat; one of the main confounding factors in these biomarker investigations, affects C15:0 and C17:0 circulating compositions independently to their relative intake. Additionally, it is unknown how changes in the dietary total-fat affects other fatty acids in circulation. Through two dietary studies with different total-fat levels but maintaining individual fatty acid compositions we were able to see how the dietary total-fat affects the fatty acids in circulation. We saw that there was a significant, proportionate, and robust decrease in the endogenous C15:0 levels with an increase in dietary total-fat. However, there was an increase in the circulating C17:0 compositions as the total-fat increased. To conclude, the dietary total-fat content and fat-type have a very complex influence on the relative compositions of circulating fatty acids, which are independent to the actual dietary fatty acid composition. Knowing how to manipulate circulating C15:0 and C17:0 composition is far-reaching in nutritional/pathological research as they highlight a dietary route to attenuate the development of metabolic disease (both by reducing risk and improving prognosis).

This study emphasizes the necessity of introducing a blockchain-based joint logistics system to strengthen the competency of medical supply chain management (SCM) and tries to develop a healthcare supply chain management (HSCM) competency measurement item through an analytic hierarchy process. The variables needed for using blockchain-based joint logistics are the performance expectations, effort expectations, promotion conditions, and social impact of the UTAUT model, and the HSCM competency results in increased reliability and transparency, enhanced SCM, and enhanced scalability. Word cloud results, analyzing the most important considerations to realize work efficiency among medical industry-related agencies, mentioned numerous words, including sudden situations, delivery, technology trust, information sharing, effectiveness, urgency, etc. This might imply the need to establish a system that can respond immediately to emergency situations during holidays. It could also suggest the importance of real-time information sharing to increase the efficiency of inventory management. Therefore, there is a need of a business model that can increase the visibility of real-time medical SCM through big data analysis. By analyzing the importance of securing reliability based on the blockchain technology in the establishment of a supply chain network for HSCM competency, we reveal that joint logistics can be achieved and synergistic effects can be created by implementing the integrated database to secure HSCM competency. Strengthening partnerships, such as joint logistics, will eventually lead to HSCM competency. In particular, HSCM should seek ways to upgrade its competitive capabilities through big data analysis based on the establishment of a joint logistics system.

Life Cycle Assessment (LCA) is a widely recognized tool used to evaluate environmental impacts of a product or process, based on the environmental inventory database and bills of material. Data quality is one of the most significant factors affecting the analysis results. However, currently most datasets in inventory databases are generic i.e., they may represent material and energy flow of a process at market average, instead of a specific process used by a manufacturer. As a result, stockholders are unable to track their supply chain to find out the actual environmental impact from each supplier and to compare the environmental performance of alternative options. In this paper, we developed a new framework i.e., blockchain based LCA (BC-LCA), where block-chain technology is adapted to secure and transmit inventory data from upstream suppliers to downstream manufacturers. With BC-LCA, more specific data can be acquired along the supply chain in a real-time manner. Moreover, the availability, accuracy, privacy, and automatic update of inventory data can be improved. A case study is provided based on an industrial supply chain, to demonstrate the utilization of BC-LCA.

During the SARS-CoV-2 pandemic, many virus-binding monoclonal antibodies have been developed for clinical and diagnostic purposes. This underlines the importance of antibodies as universal bioanalytical reagents. However, little attention is given to the reproducibility crisis that scientific studies are still facing to date. In a recent study, not even half of all research antibodies mentioned in publications could be identified at all. This should spark more efforts in the search for practical solutions for the traceability of antibodies. For this purpose, we used thirty-five monoclonal antibodies against SARS-CoV-2 to demonstrate how sequence-independent antibody identification can be achieved by simple means applied onto the protein. First, we examined the intact and light chain masses of the antibodies relative to the reference material NIST-mAb 8671. Already half of the antibodies could be identified based solely on these two parameters. In addition, we developed two complementary peptide mass fingerprinting methods with MALDI-TOF-MS that can be performed in 45 minutes and had a combined sequence coverage of over 80%. One method is based on the partial acidic hydrolysis of the protein by 5 mM of sulfuric acid at 99 °C. Furthermore, we established a fast way for a tryptic digest without an alkylation step. We were able to show that the distinction of clones is possible simply by a brief visual comparison of the mass spectra. In this work, two clones originating from the same immunization gave the same fingerprints. Later, a hybridoma sequencing confirmed the sequence identity of these sister clones. In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0 (https://gets.shinyapps.io/ABID/). This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context.

To minimise environmental impact, avoid regulatory penalties, and improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time series models due to its high dimensionality and problem solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA), and an existing manual technique used at the case site) against three deep learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) for short term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model, and then use Diebold-Mariano testing to confirm the results. Results suggests that the legacy approach used at the case site is the worst performing, and that the GRU model outperformed all other models tested.

This pilot study of Streptococcus mutans ATCC 35668 grown in media with and without polyols (erythritol) measured the resultant metabolites, including the short chain fatty acids by using head space analysis. Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose containing no polyols or either erythritol or xylitol and Streptococcus mutans (ATCC 35668) was grown aerobically. After 48 hours of growth the supernatant were harvested and centrifuged to pellet bacteria. Supernatants were removed from bacterial pellets then submitted for Short Chain Fatty Acid (SCFA) analysis with an Agilent Technologies (Santa Clara, CA 95051) system configured from three components, a 5973 mass selective detector, a 6890N gas chromatographer, and a 7697A headspace sampler. Streptococcus mutans growing in Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose but containing no polyols produced the following short chain fatty acids: methyl isovalerate, acetic acid, propionic acid, butanoic acid, pentanoic acid, ethyl butaric acid, 4-methylvaleric acid, hexanoic acid. When the Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose containing erythritol was used as media for this Streptococcus mutans strain, the following were produced: ethanol, acetoin, and acetic acid. Our results would suggest that constituents of the media may affect the bacterial metabolite production.

Over time, the agricultural sector's contribution to the nation's Gross Domestic Product and revenue is declining exponentially; the decline may be attributed to varying problems affecting the agricultural value chain resulting in low productivity. The agricultural value chain comprises a series of activities and value addition processes required to transform raw materials into useable products to maximize the final consumers' utility.¹ The agricultural value chain's marketing activities have been skewed with challenges, thus affecting agricultural productivity and agricultural sector contribution to the economy. The paper critically examines and discuss the impact of agricultural marketing to economic development, a historical review of agricultural marketing in Nigeria, challenges undermining the impact of agricultural marketing, measures to reposition agricultural marketing potential to build back better, policy recommendations to reposition the future of Nigeria's agricultural productivity. The paper aims to promote concerted efforts through knowledge dissemination to build back better through market research and facilitation and boost economic prosperity.

This paper applied case study research to design architectures for green-field supply chain integration. The integration design is based on a case study of a supply chain integration of 5 companies, operating in different, but supply chain complimenting industry sectors. The case study research is applied to design and validate the architectures in a real world scenario. The supply chain integration architectures enable the conversion of individual into integrated strategies. The architectures are categorised and the process develops into a conceptual system for identifying the correlations between individual participants’ strategic areas of interest and the integrated supply chain areas of interest. The novelty of this paper is a conceptual system for green-field supply chain integration architectures, which can be applied in real world by supply chain practitioners.

This study assesses the pursued impacts of Tulsi value chain development intervention on the livelihoods of rural poor in Uttarakhand state of India. Tulsi as an alternative livelihood, particularly for the rural poor, is less explored. With increased crop depredation of major cereal crops grown in the district by wild animals and pests, and decreasing availability of water agriculture, attempts were made to improve earnings from Tulsi as an alternative livelihood. Findings suggest that the average households’ gross profit from Tulsi farming increases by more than double within a span of two years. Total crop income of beneficiary farmers’ increases by 0.8 percent for every 1 percent increase in Tulsi income. Intervention helped enhance productivity of Tulsi, thereby enhancing earnings from Tulsi farming. Most importantly, intervention has shown a tremendous adoption rate. Towards the end of the intervention, the value chain work was out-scaled to another 19 villages in Chamoli district, thereby reaching out to more than 400 households.

Although the signaling function of Na/K ATPase is been studied for decades, the chasm between the pumping function and the signaling function of Na/K – ATPase is still an open issue. This article explores the relationship between ion pumping and signaling with attention to the amplification of oxidants through this signaling function. Starting with some experimental observations published by our laboratories and others, we develop some predictions regarding cellular oxidant stress.

Statistics of languages are calculated by counting characters, words, sentences, word rankings. Some of these random variables are also the main “ingredients” of classical readability formulae. Revisiting the readability formula of Italian, known as GULPEASE, shows that of the two terms that determine the readability index G – the semantic index G_C, proportional to the number of characters per word, and the syntactic index G_F, proportional to the reciprocal of the number of words per sentence −, G_F is dominant because G_C is, in practice, constant for any author throughout seven centuries of Italian Literature. Each author can modulate the length of sentences more freely than he can do with the length of words, and in different ways from author to author. For any author, any couple of text variables can be modelled by a linear relationship y=mx, but with different slope m from author to author, except for the relationship between characters and words, which is unique for all. The most important relationship found in the paper is, in author’s opinion, that between the short−term memory capacity, described by Miller’s “7∓2 law”, and the word interval, a new random variable defined as the average number of words between two successive punctuation marks. The word interval can be converted into a time interval through the average reading speed. The word interval is spread in the same of Miller’s law, and the time interval is spread in the same range of short−term memory response times. The connection between the word interval (and time interval) and short−term memory appears, at least empirically, justified and natural, and should further investigated. Technical and scientific writings (papers, essays etc.) ask more to their readers. A preliminary investigation of these texts shows clear differences: words are on the average longer, the readability index G is lower, word and time intervals are longer. Future work done on ancient languages, such as Greek or Latin, could bring us a flavor of the short term−memory features of these ancient readers.

With the Paris Agreement, countries are obliged to report greenhouse gas (GHG) emission reduc-tions, which will ensure that the global temperature increase is maintained well below 2C. The Parties will report their Nationally Determined Contributions in terms of plans and progress to-wards these targets during the postponed COP26 in Glasgow in November 2021. These commit-ments however do not take significant portions of the consumption related emissions related to countries imports in to account. Similarly, the majority of companies that report their emissions to CDP also do not account for their embodied value-chain related emissions. Municipalities, on the path towards carbon neutrality in accordance with the methods outlined by C40, also do not in-clude imported and embodied CO2e in their total emission tallies. So, who is responsible for these emissions - the producer or the consumer? How can we ensure that the NDC's, municipalities and companies reduction targets share the responsibility of the emissions in the value-chain thus en-suring that targets and plans become, sustainable, climate fair, and just in global value chains? Today the responsibility lays with the producer, which is not sustainable. We have the outline for the tools needed to quantify and transparently share the responsibility between producers and consumers at corporate, municipal and national level based on an improved understanding of the attendant sources, causes, flows and risks og GHG emissions globally. Hybrid LCA/EEIO models can for example be further developed. This will, in the end, enable everyday consumption to support a more sustainable, green and low carbon transition of our economy.

It was demonstrated during the past decade that ultra-short intense laser pulse tightly focused deep inside a transparent dielectric generates the energy density in excess of several MJ/cm$^3$. Such energy concentration with extremely high heating and quenching rates leads to unusual solid-plasma-solid transformation paths overcoming kinetic barriers to formation of previously unknown high-pressure material phases, which are preserved in the surrounding pristine crystal. These results were obtained with the pulse of Gaussian shape in space and in time. Recently it was shown that the Bessel-shaped pulse could transform much larger amount of a material and allegedly create even higher energy density than that was achieved with the Gaussian (GB) pulses. Here we present a succinct review of previous results and discuss the possible routes for achieving higher energy density employing the Bessel beams (BB) and take advantage of its unique properties.

The out-of-equilibrium dynamics of many body systems has recently received a burst of interest, also due to experimental implementations. The dynamics of both observables, such as magnetization and susceptibilities, and quantum information related quantities, such as concurrence and entanglement entropy, have been investigated under different protocols bringing the system out of equilibrium. In this paper we focus on the entanglement entropy dynamics under a sinusoidal drive of the tranverse magnetic field in the 1D quantum Ising model. We find that the area and the volume law of the entanglement entropy coexist under periodic drive for an initial non-critical ground state. Furthermore, starting from a critical ground state, the entanglement entropy exhibits finite size scaling even under such a periodic drive. This critical-like behaviour of the out-of-equilibrium driven state can persist for arbitrarily long time, provided that the entanglement entropy is evaluated on increasingly subsytem sizes, whereas for smaller sizes a volume law holds. Finally, we give an interpretation of the simultaneous occurrence of critical and non-critical behaviour in terms of the propagation of Floquet quasi-particles.

In recent years supply chains have evolved into huge ecosystems, demanding trust, provenance, and data privacy. Since blockchain technology (BCT) allows for the development of a distributed environment, it is ideal for supply chain management (SCM) applications. However, concerns regarding data privacy have impeded the development of blockchains. Despite the fact that some blockchains can restrict participants to read and write data, the blockchain’s transparency makes protecting sensitive data challenging. To solve the data privacy challenge, this paper proposes a framework, AccessChain, that is an SCM access control framework based on an attribute-based access control (ABAC) model that restricts access to competing parties while allowing for network scalability. This proposed AccessChain model has two types of ledgers in the system: local and global. Local ledgers are used to store business contracts between stakeholders and the ABAC model management, whereas the global ledger is used to record transaction data. AccessChain can enable decentralized, fine-grained and dynamic access control management in SCM when combined with the ABAC model and BCT. This paper’s experimental results illustrate that high throughput can be achieved in a large-scale request environment while maintaining data privacy and sustaining a scalable network.

We investigated the role of the human medio-temporal complex (hMT+) in the memory encoding and storage of a sequence of four coherently moving RDKs by applying repetitive transcranial magnetic stimulation (rTMS) during an early or late phase of the retention interval. Moreover, in a second experiment we also tested whether disrupting the functional integrity of hMT+ during the early phase impaired the precision of the encoded motion directions. Overall, results showed that both recognition accuracy and precision were worse in middle serial positions, suggesting the occurrence of primacy and recency effects. We found that rTMS delivered during the early (but not the late) phase of the retention interval was able to impair not only recognition of RDKs, but also the precision of the retained motion direction. However, such impairment occurred only for RDKs presented in middle positions along the presented sequence, where performance was already closer to chance level. Altogether these findings suggest an involvement of hMT+ in the memory encoding of visual motion direction. Given that both position sequence and rTMS modulated not only recognition but also precision of the stored information, these findings are in support of a model of visual short-term memory with a variable resolution of each stored item, consistent with the assigned amount of memory resources, and that such item-specific memory resolution is supported by the functional integrity of area hMT+.

Global financial investments in energy production and consumption are significant since all aspects of a country's economic activity, and development require energy resources. In this paper, we assess the investment trends in the global energy sector during, before and after financial crises of 2008 using two data sources: (1) Dealogic database providing cross‐border mergers and acquisitions (M&As), and (2) fDi Intelligence fDi Markets database providing greenfield (GF) foreign direct investments (FDIs). We highlight the changing role of China and compare its M&A and GF FDI activities to those of the United States, Germany, UK, Japan and others during this period. We analyze the investments along each segment of the energy supply chain of these countries to highlight the geographical origin and destination, sectoral distribution, and cross‐border M&As and GF FDI activities. Our paper shows that while energy accounts for nearly 25% of all GF FDI, it only accounts for 4.82% of total M&A FDI activity in the period 1996-2016. China's outbound FDI in the energy sector started its ascent around the time of the global recession and had accelerated in the post-recession phase. In the energy sector, the development of China's outbound cross‐border M&As is similar to USA or UK, located mostly in the developed countries in the west, while their outbound GF investments are spread across many countries around the world. Also, China's outbound energy M&As are concentrated in certain segments (extraction, and electricity generation) while their GF covers all segments of the energy supply chain.

Cardiovascular disease is the leading cause of death worldwide. A key factor in assessing the risk of cardiovascular disease is left ventricular functional evaluation. Left ventricular (LV) systolic function is evaluated by measuring the left ventricular ejection fraction (LVEF) using echocardiography data. Therefore, quick and accurate left ventricle segmentation is important for estimating the LVEF. However, it is difficult to accurately segment the left ventricle due to changes in the shape and area of the left ventricle during cardiac cycles. In this study, we proposed a framework that considers changes in the shape and area of the left ventricle during the cardiac cycle by applying the convolutional long short-term memory (CLSTM) approach. In addition, we evaluated the left ventricular segmentation and multidimensional quantification of the proposed system in comparison to manual and automated segmentation methods. In addition, to assess the validity of CLSTM, the values of multi-dimensional quantification metrics were compared and analyzed using graphs and Bland–Altman plots on a frame-by-frame basis. We demonstrated that the CLSTM method effectively segments the left ventricle by considering the LV activity. In conclusion, we demonstrated that LV segmentation based on our framework may be utilized to accurately estimate LVEF values.

The focus of this paper is on supply chain strategy formulation. A conceptual theory approach is used for investigating and identifying the relationship between multiple elements, dimensions, forces and factors that influence and affect the supply chain strategy formulation in Greenfield context, specific to the slate mining industry. The research study involved secondary data review and series of 20 qualitative interviews, followed by 2 group discussions, one with mining and transportation experts external to the supply chain and one group discussion with supply chain internal experts. Through critical analysis, a number of problems emerge and the process of addressing these problems, results in a new framework for evaluating the relationship between business and supply chain strategy, specific to Greenfield project and integration design context.

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.

Abstract: Many countries around the world suffer from the lack of a sea port directly linked to the rest of the world. Such countries are called "landlocked countries". This leads to Weak competitiveness of their products in the global market, as well as to the high cost of the imports. Africa has the largest share of these countries, with 16 of the 43 landlocked countries around the world. The aim of this paper is to propose a general framework for criteria that can be used to choose between ports in transit countries that can be used for import or export. These criteria are related to the assessment of the sea ports in terms of infrastructure and tariffs. It is also related to transport infrastructure from the transit country to the landlocked country and the level of safety. The study identified nine criteria that could be used to compare between ports in transit countries. Using Full Consistency Method (FUCOM) to evaluate those criteria showed that the number of navigation lines is the most important criteria followed by the port service level.

The zebrafish Danio rerio is suitable to study gametes as a model organism. There were > 70% of zebrafish spermatozoa activated, because they were contaminated with urine or excrement. The movement of spermatozoon in water was propagated along the flagellum at 16 s after sperm activation, then damped from the end of the flagellum for 35 s and fully disappear at 61 s after activation. For artificial fertilization, milt must be added to an extender, which stops the movement of sperm and keeps the sperm motionless until fertilization. E400 was shown to be the most suitable extender as it allows to store sperm for fertilization for 6 to 12 h at 0-2oC. Sperm motility decreased only to 36% at 12 h post stripping (HPS) for E400 extender and to 19% for Kurokura extender. To achieve an optimal level of fertilization and hatching, a test tube with a well-defined amount of 6,000,000 spermatozoa in E400 extender per 100 eggs and 100 µl of activation solution has proved to be more successful than using a Petri dish. The highest fertilization and hatching rates reached 80% and 40-60%, respectively, with milt stored for 1.5 h in E400 extender at 0-2oC.

One of the most burning topics in cybersecurity in 2023 will undoubtedly be the compliance with the Software Bill of Materials. Since the US president issued the Executive Order 14028 on Improving the Nation’s Cybersecurity, software developers have prepared and bills are transmitted to vendors, customers, and users, but they don’t know what to do with the reports they are getting. In addition, since software developers have identified the values of the Software Bill of Materials, they have been using the reports extensively. This article presents an estimate of 270 million requests per month, just from form one popular tool to one vulnerability index. This number is expected to double every year and a half. This simple estimate explains the urgency for automating the process. We propose solutions based on artificial intelligence and machine learning, and we base our tools on the existing FAIR principles (Findable, Accessible, Interoperable, and Reusable). This methodology is supported with a case study research and Grounded theory, for categorising data into axis, and for verifying the values of the tools with experts in the field. We showcase how to create, and share Vulnerability Exploitability eXchange data, and automate the Software Bill of Materials compliance process with AI models and a unified computational framework combining solutions for the following problems: (1) the data utilisation problem, (2) the automation and scaling problem, (3) the naming problem, (4) the alignment problem, (5) the pedigree, and provenance problem, and many other problems that are on the top of mind for many security engineers at present. The uptake of these findings will depend on collaborations with government and industry, and on the availability and the ease of use of automated tools.

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

The conformational preferences of the ester group have the potential to facilitate the large amplitude folding of long alkyl chains in the gas phase. They are monitored by Raman spectroscopy in supersonic jet expansions for the model system methyl butanoate, after establishing a quantitative relationship to quantum-chemical predictions for methyl methanoate. This requires a careful analysis of experimental details, and a simulation of the rovibrational contours for near-symmetric top molecules. The technique is shown to be complementary to microwave spectroscopy in quantifying coexisting conformations. It confirms that a C-O-C(=O)-C-C chain segment can be collapsed into a single all-trans conformation by collisional cooling, whereas alkyl chain isomerism beyond this five-membered chain largely survives the jet expansion. This sets the stage for the investigation of linear alkyl alkanoates in terms of dispersion-induced stretched-chain to hairpin transitions by Raman spectroscopy.

Clean use of photons from light to activate chemical reactions offer many possibilities in different fields, from chemistry and biology to materials science and medicine. This review article describes the advances carried out in last decades toward the phototriggered synthesis of single-chain polymer nanoparticles (SCNPs) as soft nanomaterials with promising applications in enzyme-mimicking catalysis and nanomedicine, among other different uses. First, we summarize different strategies developed to synthesize SCNPs based on photoactivated intrachain homocoupling, phototriggered intrachain heterocoupling and photogenerated collapse induced by external cross-linker. Next, we comprehensively review the emergent topic of photoactivated multifolding applied to SCNP construction. Finally, we conclude by summarizing recent strategies towards phototriggered disassembly of SCNPs.

The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method has been previously proposed by our laboratory for geographically distributed PV systems using motion estimation. This study focuses on an important parameter for estimating the proposed motion and optimizing the parameter. This parameter is important because it is associated with the smoothness of the vector field, which is the result of motion estimation and influences the forecasting accuracy. In the periods with drastic power output changes, the evaluation was conducted on 101 PV systems located within a circle of 15-km radius in the Kanto region of Japan. The results indicate that the absolute mean error of the proposed method with the optimized parameter is 10.3%, whereas that of the persistent prediction method is 23.7%. Therefore, the proposed method is effective in forecasting for periods when PV output changes drastically in a short time.

The lean supply chain of construction engineering projects is to achieve the maximum satisfaction of the owners' needs in order to effectively achieve the goal of supply chain management. This paper explores an effective method of lean supply chain cost management for construction engineering projects with target cost management, so that each participating unit on the supply chain node can fully utilizes its core competencies to minimize internal consumption and waste, and achieve the optimal overall efficiency of the supply chain. According to the requirements of the goal planning theory of the construction project company, establish a lean supply chain cost planning system for the construction project, realize the basic model of the lean supply chain cost management of the construction project, and set the target cost from the lean project of the construction project. The technical decomposition is established by the process of cost decomposition and cost pressure transmission and sub-target cost planning.

Irish beef farms have experienced poor viability longitudinally, with officially acknowledged ‘crisis’ levels in 2013. In response, beef Producer Organisation (PO) legislation was introduced. Through the lens of international evidence in the literature about how POs function, this paper presents an analysis of Irish stakeholders’ views in the context of a public consultation process. While stakeholders indicate the need for individual POs to collaborate, little emphasis is placed on other factors necessary for success: collaboration with other chain actors; and market differentiation of products. Stakeholders identified primary threats as poor engagement on the part of both farmers and processors/purchasers.

Short chain quinones (SCQs) have been identified as potential drug candidates against mitochondrial dysfunction, which is largely dependent on their reversible redox characteristics of the active quinone core. We recently synthesized a SCQ library of > 148 naphthoquinone derivatives and identified 16 compounds with enhanced cytoprotection compared to the clinically used benzoquinone idebenone. One of the major drawbacks of idebenone is its high metabolic conversion in the liver, which significantly restricts is therapeutic activity. Therefore, this study assessed the metabolic stability of the 16 identified naphthoquinone derivatives 1-16 using hepatocarcinoma cells in combination with an optimized reverse-phase liquid chromatography (RP-LC) method. Most of the derivatives showed significantly better stability than idebenone over 6 hours (p < 0.001). By extending the side-chain of SCQs, increased stability for some compounds was observed. Metabolic conversion from the derivative 3 to 5 and reduced idebenone metabolism in the presence of 5 were also observed. These results highlight the therapeutic potential of naphthoquinone-based SCQs and provide essential insights for future drug design, prodrug therapy and polytherapy, respectively.

The world has now entered into a replacement era of genomics due to the continued advancements within the next generation high throughput sequencing technologies, which incorporates sequencing by synthesis-fluorescent in place sequencing (FISSEQ), pyrosequencing, sequencing by ligation using polony amplification, supported oligonucleotide detection (SOLiD), sequencing by hybridization alongside sequencing by ligation, and nanopore technology. Great impacts of those methods are often seen for solving the genome related problems of plant and Animalia which will open the door of a replacement era of genomics. This might ultimately overcome the Sanger sequencing that ruled for 30 years. NGS is predicted to advance and make the drug discovery process more rapid.

We present a new analytical method to find the asymptotic stable equilibria states based on the Markov chain technique. We reveal this method on the SIR-type epidemiological model that we developed for viral diseases with long-term immunity memory pandemic. This is a large-scale model containing 15 nonlinear ODE equations, and classical methods have failed to analytically obtain its equilibria. The proposed method is used to conduct a comprehensive analysis by a stochastic representation of the dynamics of the model, followed by finding all asymptotic stable equilibrium states of the model for any values of parameters and initial conditions.

We addressed the frequent occurrence of mixed-chain lipids in biological membranes and their impact on membrane structure by studying several chain-asymmetric phosphatidylcholines and the highly asymmetric milk sphingomyelin. Specifically, we report trans-membrane structures of the corresponding fluid lamellar phases using small-angle X-ray and neutron scattering, which were jointly analyzed in terms of a membrane composition-specific model, including a headgroup hydration shell. Focusing on terminal methyl groups at the bilayer center we found a linear relation between hydrocarbon chain length mismatch and the methyl-overlap for phosphatidylcholines, and a non-negligible impact of the glycerol backbone-tilting, letting the sn1-chain penetrate deeper into the opposing leaflet by half a CH2 group. That is, penetration-depth differences due to the ester-linked hydrocarbons at the glycerol backbone, reported previously for gel phase structures also extend to the physiological more relevant fluid phase, but are significantly reduced. Moreover, milk sphingomyelin was found to follow the same linear relationship suggesting a similar tilt of the sphingosine backbone. Complementary performed molecular dynamics simulations revealed that there is always a part of the lipid tails bending back, even if there is a high interdigitation with the opposing chains. This suggests that hydrocarbon chain interdigitation plays only a minor role in transbilayer coupling. For both cases of adaption to chain length mismatch, chain-asymmetry has a large impact on hydrocarbon chain ordering, inducing disorder in the longer of the two hydrocarbons.

The multi-material design and the adaptability of a modern process chain require joining connections with specifically adjustable mechanical, thermal, chemical or electrical properties, whereby previous considerations have focused primarily on the mechanical properties. With clinching, the multitude of possible combinations of requirements, materials and component or joint geometry makes it impossible to determine these joint properties empirically. As a result of the established and empirically based procedure, no model exists to date that considers all questions of joinability, i.e. the materials (suitability for joining), the design (joining safety) and the production (joining possibility) and allows a calculation of the achievable properties. It is therefore necessary to describe the physical properties of the joint as a function of the three bonding mechanisms force closure, form closure and material closure in relation to the application. This approach enables the illustration of the relationships along the causal chain "joint requirement - binding mechanism - joining parameters". In this way the adaptability of the mechanical joining technology can be improved. A geometric comparison is made using metallographic cross sections, of clinched joints of the combination of aluminum and steel. The torsional testing of the rotationally symmetric clinching points for detection of the mechanical stress state are qualified as examination method and technological test. By measuring the electrical resistance in the base material, in the clinch joint and during the production cycle (after clinching, before precipitation hardening and after precipitation hardening), this change in the stress state can also be detected.

Much evidence has accumulated over the past decade in favor of a significant association between dysbiosis, neuroinflammation and neurodegeneration. Presently, the pathogenetic mechanisms triggered by molecules produced by the altered microbiota, also responsible for the onset and evolution of Alzheimer Disease will be described. Our attention will be focused on the role of astrocytes and microglia. Numerous studies have progressively demonstrated how these glial cells are important to ensure an adequate environment for neuronal activity in healthy conditions. Furthermore, it is becoming evident how both cell types can mediate the onset of neuroinflammation and lead to neurodegeneration when subjected to pathological stimuli. Based on this information, the role of major microbiota products in shifting the activation profiles of astrocytes and microglia from a healthy to a diseased state will be discussed focussing on Alzheimer Disease pathogenesis.

DNA sequencing methods were first developed more than 20 years ago with the publication of two approaches to sequencing methodology that became known as Sanger sequencing (1), based on enzymatic synthesis from a single-stranded DNA template with chain termination using dideoxynucleotides (ddNTPs) and Maxim-Gilbert sequencing (2),which involved chemical degradation ofend-radio-labeled DNA fragments. Both methods relied on four-lane,high resolution polyacrylamide gel electrophoresis to separate the labeled fragment and allow the base sequence to be read in a staggered ladder-like fashion. Sanger sequencing was technically easier and faster, and thus became the main DNA sequencing method for the vast majority of applications.

The traditional sequent peak algorithm (SPA) was used to assess the reservoir volume (VR) for comparison with deficit volume, DT, (subscript T representing the return period) obtained from the drought magnitude (DM) based method with draft level set at the mean annual flow on 15 rivers across Canada. At an annual scale, the SPA based estimates were found to be larger with an average of nearly 70% compared to DM based estimates. To ramp up DM based estimates to be in parity with SPA based values, the analysis was carried out through the counting and the analytical procedures involving only the annual SHI (standardized hydrological index, i.e. standardized values of annual flows) sequences. It was found that MA2 or MA3 (moving average of 2 or 3 consecutive values) of SHI sequences were required to match the counted values of DT to VR. Further, the inclusion of mean, as well as the variance of the drought intensity in the analytical procedure, with aforesaid smoothing led DT comparable to VR. The distinctive point in the DM based method is that no assumption is necessary such as the reservoir being full at the beginning of the analysis - as is the case with SPA.

In recent years, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns, policy decision support, etc. because the information on alighting stops of users is missing. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. Various studies to overcome this limitation have been conducted in South Korea and other countries to develop es-timation methodologies of alighting stops. Even existing studies introduce an advanced method, we found the margin for better accuracy by combining various estimation methodologies for estimating alighting stops. This study reviewed previously conducted studies to classify data with missing alighting stop information into trip types and then applied an appropriate alighting stop estimation methodology for the characteristics of each trip type by stage. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checked the accuracy for each standard of allowable error for sensitivity analysis. Furthermore, the number of trips, accuracy, and valid tag rate were checked for each type to examine the need for classifying the trip types. Finally, our evaluation also examines the impact of classifying trip types on estimation accuracy. The evaluation criteria are accuracy of the number of trips and valid tag rate. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can es-timate users’ destinations more accurately than previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.

The statistical analysis of impact of the top 50 solar flares of X-class (1997-2023) on the global seismic activity, as well as on the earthquake preparation zones located in illuminated part of the globe and in the area of 5000 km radius around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases the increase of seismicity is observed, especially in the region around the subsolar point (up to 38%) during 10 days after the solar flare in comparison with preceding 10 days. The case study of aftershock sequence of strong M=9.1 earthquake (Su-matra-Andaman Islands, 26.12.2004) after the solar flare of X7.2 class (20.01.2005) demonstrated that the number of aftershocks with magnitude M≥2.5 increases more than 20 times after the solar flare with a delay of 7 days. For the case of the Darfield earthquake (M=7.1, 04.09.2010, New Zealand) it was shown that strong solar flares of class X and M probably triggered two strong aftershocks (M>6) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from point of view of the fault electrical conductivity and orientation. Based on the obtained results the possible application of natural electromagnetic triggering of earthquakes is discussed for a short-term earthquake prediction using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

The most extensive urban growths in the next 30 years are expected to occur in developing countries. Lagos, Nigeria - Africa’s second most populous megacity- is a prime example. To achieve more sustainable and resilient cities, there is a need for modeling the urban growth patterns of major cities and analyzing their implications. In this study, the urban growth of Lagos state was modeled using the Multi-Layer Perceptron (MLP) neural network for the transition modeling and the Markov Chain analysis for the change prediction, achieving a model accuracy of 81.8%. An innovative visual validation of the model results using the ArcGIS was combined with kappa correlation statistics. The results show that by 2031, built-up areas will be the most spatially extensive LULC class in the study area with percentage coverage of 34.1% as opposed to 9% in 1986. The coverage of bare areas is also expected to increase by 53% between 2016 and 2031. Conversely, 24.9% and 68.3% loss of forestlands and wetlands respectively, are expected between 2016 and 2031. In view of the 11^th goal of SDGs which focuses on achieving sustainable cities and communities, the objectives of African Union’s Agenda 2063, and based on the urban growth trends observed, the study recommends a prioritization of vertical expansion as opposed to the current horizontal urban growth trends in the study area.

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

Petrochemicals, which convert oil and gas into products such as plastics, are fundamental to modern societies. Chemists recognize their role in the design of materials and the adverse effects that these may have on the environment, preventing sustainable development. Several methodological frameworks and sustainability assessment approaches have been developed to evaluate the resources used in the petrochemical sector in terms of environmental costs. A combination of Life Cycle Assessment and Emergy Accounting - to assess the environmental support for resource use - is applied in this study of the PET production chain in Europe. The Unit Emergy Values of several intermediates are calculated or updated to facilitate discernment of the quality of energy used and the processes' efficiency. Several routes for synthesizing renewable para-xylene and ethylene glycol from biomass are discussed and confronted with the efforts focused on recycling and recovering the final product providing concurrently a procedure and a valuable data set for future CP actions. The results show that understanding the efficiencies changing across the production chain may help stakeholders make wise choices as to where and when interventions to promote a circular economy are most effective along a petrochemical production chain.

^{We consider the collective pricing orders in a minimum supply chain that is composed of a monopolistic manufacturer and many retailers that belong to the same chain store firm. The retailers have the freedom to raise or lower the local price. The chain store firm sets up the commercial rules for local retail stores to maximize its total payoff. The monopolistic manufacturer firm controls the total quantity supplied for the market to achieve maximum benefits. We applied the two dimensional Ising model in statistical physics to map the collective distribution of microscopic strategy of local retailers into the macroscopic total payoff of the chain store firm. The local stores choose to raise the price or lower the price based their own mind when the supply in market surpasses the demand. When the supply in market is far less than the demand, the stores synchronously raise prices, even though a local store only have the incomplete information of their nearest neighboring supermarket. We find the critical equation for the balance point between the action of supplier and the action of chain store management based on game theory and statistical physics. The critical equation can identify the Nash equilibrium point of the non-cooperative game between the manufacturer and the chain-store seller, and reveal different levels of collective operations. This statistical physics method also holds for more complicate supply chains and economic systems.}

The folding of certain proteins (e.g., enzymes) into perfectly defined 3D conformations via multi-orthogonal interactions is critical to their function. Concerning synthetic polymers chains, the “folding” of individual polymer chains at high dilution via intra-chain interactions leads to so-called single-chain nanoparticles (SCNPs). This review article describes the advances carried out in recent years in the folding of single polymer chains into discrete SCNPs via multi-orthogonal interactions using different reactive chemical species where intra-chain bonding only occurs between groups of the same species. First, we summarize results from computer simulations of multi-orthogonally folded SCNPs. Next, we comprehensively review multi-orthogonally folded SCNPs synthesized via either non-covalent bonds or covalent interactions. Finally, we conclude by summarizing recent research about multi-orthogonally folded SCNPs prepared through both reversible (dynamic) and permanent bonds.

Since its initial development in the 1970’s by Phil Burke and his collaborators, the R-matrix theory and associated computer codes have become the de facto approach for the calculation of accurate data for general electron-atom/ion/molecule collision and photoionization processes. The use of a non-orthonormal set of orbitals based on B-splines, now called the B-spline R-matrix (BSR) approach, was pioneered by Zatsarinny. It has considerably extended the flexibility of the approach and improved particularly the treatment of complex many-electron atomic and ionic targets, for which accurate data are needed in many modelling applications for processes involving low-temperature plasmas. Both the original R-matrix approach and the BSR method have been extended to the interaction of short, intense electromagnetic (EM) radiation with atoms and molecules. Here we provide an overview of the theoretical tools that were required to facilitate the extension of the theory to the time domain. As an example of a practical application, we show results for two-photon ionization of argon by intense short-pulse extreme ultraviolet radiation

We quantitatively investigated the statistical behaviors of semiflexible polymer chains, which are simultaneously subjected to force stretching and rectangular tube confinement. Based on the wormlike chain model and Odijk deflection theory, we derived a new deflection length, by which new compact formulas are obtained for the confinement free energy and force-confinement-extension relation. These newly derived formulas have been justified by numerical solutions of an eigenvalue problem associated with the Fokker-Planck governing equation and extensive Brownian dynamics simulations based on the so-called Generalized Bead-Rod (GBR) model. We found that, comparing to the classical deflection theory, these new formulas are valid for a much extended range of the confinement-size /persistence-length ratio, and have no adjustable fitting parameters for sufficient long semiflexible chains in the whole deflection regime.

The degree of collaboration among supply chain partners and the structure of the network are important determinants of the level of satisfaction customers derive from the products or services. However, the effects of these dimensions on customer satisfaction at the downstream section of the supply chain remain under-researched in Nigeria. This study precisely examined the effects of collaboration and supply chain design on customers’ satisfaction at the downstream end of the chain using Ekiti State as study area. The study employed descriptive survey design with the use of structured Likert scale questionnaire administered to 381 retailers of noodles in Ekiti State. The research hypotheses were analysed using simple linear regression as statistical technique with the aid of SPSS version 22.0. At the end of the study, it was observed that both collaboration and supply chain design were significant predictors of customers’ satisfaction of instant noodles in Ekiti State. However, collaboration among supply chain partners emerged as stronger determinant of customers’ satisfaction than supply chain design. The study concludes that these two practices of supply chain management are highly important criteria any manufacturing firm especially in the noodles industry must pay close attention to in order to satisfy her consumers.

To contribute to small and medium enterprises (SMEs) sustainable transition into the circular economy, the study proposes the activation of organizational learning (OL) processes – denoted here as multi-level knowledge creation, transfer, and retention processes – as a key phase in introducing circular business models (CBMs) at SME and supply chain (SC) level. The research employs a mixed-method approach, using the focus group methodology to identify contextual elements impacting on CBM-related OL processes, and a survey-based evaluation to single out the most frequently used OL processes inside Italian construction SMEs. As main result, a CBM-oriented OL multi-level model offers a fine-grained understanding of contextual elements acting mutually as barriers and drivers for OL processes, as possible OL dynamics among them. The multi-level culture construct – composed of external stakeholders’, SC stakeholders’, and organizational culture – identify the key element to activate CBM-oriented OL processes. Main implications are related to the identification of cultural, structural, regulatory, and process contextual elements across the external, SC, and organizational levels, and their interrelation with applicable intraorganizational and interorganizational learning processes. The proposed model would contribute to an improved implementation of transitioning into the circular economy utilizing sustainable business models in the construction SMEs.

Dementia patients express a set of problematic and deteriorating symptoms, along self-care dependency. Overtime, the mental health of family caregivers of persons with dementia may suffer, putting them at a high risk for psychopathology, which may be associated with endangered wellbeing of demented people. This cross-sectional study examined the psychometric properties of the Depression Anxiety Stress Scale 8-items (DASS-8), DASS-12, DASS-21 in a convenient sample of 571 caregivers from northern Italy and southern Switzerland (Mean age = 53, SD = 12, range = 24–89 years). A bifactor structure of the three measures had the best fit; some items of the DASS-12/DASS-21 failed to load on their domain-specific factors. The three-factor structure was invariant across various groups (e.g., gender, education, etc.), expressed adequate reliability and convergent validity, and had strong positive correlation with the 3-item UCLA Loneliness Scale (UCLALS3). Dementia type had no effect on distress scores, which were higher among females, adult children caregivers, those caring for dependent patients, and those who received help with care. For 54.9 and 38.8% of the latter, care was provided by relatives and health professionals, respectively. Since the DASS-8 expresses adequate psychometrics comparable with the DASS-21, it may be used as a brief measure of distress in this population.

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.