In order to investigate the effect of the Ni element on the properties of AlCoCrFeMn HEAs, this experiment prepared AlCoCrFeMn and AlCoCrFeNiMn HEAs by using a laser-ignition self-propagation sintering technique with an equal molar ratio. And analyzed the effect of the Ni element on the microstructure of AlCoCrFeMn HEAs by using a metallurgical optical microscope (OM), scanning electron microscope (SEM), energy spectroscopic analysis (EDS), X-ray diffraction (XRD), and other experiments. Characterization equipment was used to analyze the effect of the Ni element on the microstructure, physical phase structure, wear resistance, compressive properties, and corrosion resistance of AlCoCrFeMn HEA materials. The results show that after the addition of the Ni element, the AlCoCrFeNiMn HEA changes from a single BCC phase to one consisting of BCC and a small amount of an FCC phase, with an equiaxial organization, and the yield strength reaches 780 MPa and the compressive strength is 3920 MPa. The corrosion rate is 2.08 × 10⁻³ mm/a, and the corrosion resistance and mechanical properties are greatly increased. Full article

We present a pulse-preserving multilayer-based extreme-ultraviolet (XUV) monochromator providing ultra-narrow bandwidth ($\Delta E<0.6\mathrm{eV}$, $E_{\mathrm{c}}=92\mathrm{eV}$) and compact footprint ($28\times 10{\mathrm{cm}}^2$) for easy integration into high-harmonic generation (HHG) or free-electron laser (FEL) sources. The temporal resolution of the novel design supports pulse durations of typical pump–probe setups in the femtosecond and attosecond regime, depending on the mirror design and focusing geometries over the tuning range of the monochromator. The theoretical design is analyzed and experimentally characterized in a laser-driven HHG setup. Full article

Bohai X oilfield has reached the extra-high water cut stage of more than 95%, dominated by the bottom water reservoir. The oilfield mainly adopts horizontal-well exploitation, with the characteristics of high difficulty and low success rate for well water plugging. To solve the above problem, the segmented production technology of horizontal wells was developed to guide oilfield applications and tap their potential. In the segmented design stage, the horizontal section is objectively segmented by drilling condition analysis, optimally based on drilling through interlayers or permeability discrepancy formation, simultaneously combined with the numerical simulation method. When implementing measures, annulus chemical packer materials are squeezed between segments to effectively inhibit the fluid flow between the open hole and the sand-packing screen pipe. Moreover, the packers are used to seal between segments to effectively restrain the flow between the screen and the central tube, achieving the establishment of compartments. In the production process, the valve switch on the central tube can be independently controlled by a remotely adjustable method to achieve optimal production. This segmented production technology was successfully tested for the first time in Bohai oilfield. Up to now, a total of six compartment measures have been implemented, remarkably decreasing water cut and increasing oil production for horizontal wells in the bottom water reservoir. This method does not require water testing, and the optimal production section can be chosen through segmented independent production, greatly improving the success rate of water-plugging measures for horizontal wells. This technology opens up a new mode for the efficient development of horizontal wells in bottom water reservoirs and is planned to be widely promoted and applied in similar oilfields. Full article

Pro-environmental behaviors have been confirmed as an essential source of sustainable development. However, there is limited research exploring its antecedents from the perspective of organizational management mechanisms (e.g., environmental leadership). This article draws on upper-echelon and self-consistency theories to explain why environmental leadership induces environmental corporate social responsibility policy adoption, which causes employees’ environmental behavior. In addition, the relationship is mediated by environmental identity. This article collected empirical data from 101 technology firm employees, and the results support all hypotheses. Finally, this article addresses a new research stream of leadership concerning pro-environmental behaviors. It demonstrates a novel pathway to promote pro-environmental behaviors through adopting environmental corporate social responsibility policies, which guides a new research direction in terms of environmental organization behaviors. Full article

Simultaneous dual-contrast imaging of iodine and bismuth has shown promise in prior phantom and animal studies utilizing spectral CT. However, it is noted that in previous studies, Pepto-Bismol has frequently been employed as the source of bismuth, exceeding the recommended levels for human subjects. This investigation sought to assess the feasibility of visually differentiating and precisely quantifying low-concentration bismuth using clinical dual-source photon-counting CT (PCCT) in a scenario involving both iodinated and bismuth-based contrast materials. Four bismuth samples (0.6, 1.3, 2.5, and 5.1 mg/mL) were prepared using Pepto-Bismol, alongside three iodine rods (1, 2, and 5 mg/mL), inserted into multi-energy CT phantoms with three different sizes, and scanned on a PCCT system at three tube potentials (120, 140, and Sn140 kV). A generic image-based three-material decomposition method generated iodine and bismuth maps, with mean mass concentrations and noise levels measured. The root-mean-square errors for iodine and bismuth determined the optimal tube potential. The tube potential of 140 kV demonstrated optimal quantification performance when both iodine and bismuth were considered. Distinct differentiation of iodine rods with all three concentrations and bismuth samples with mass concentrations ≥ 1.3 mg/mL was observed across all phantom sizes at the optimal kV setting. Full article

Deep defects in the long-wave infrared (LWIR) HgCdTe heterostructure photodiode were measured via deep-level transient spectroscopy (DLTS) and photoluminescence (PL). The n⁺-P⁺-π-N⁺ photodiode structure was grown by following the metal–organic chemical vapor deposition (MOCVD) technique on a GaAs substrate. DLTS has revealed two defects: one electron trap with an activation energy value of 252 meV below the conduction band edge, located in the low n-type-doped transient layer at the π-N⁺ interface, and a second hole trap with an activation energy value of 89 meV above the valence band edge, located in the π absorber. The latter was interpreted as an isolated point defect, most probably associated with mercury vacancies (V_Hg). Numerical calculations applied to the experimental data showed that this V_Hg hole trap is the main cause of increased dark currents in the LWIR photodiode. The determined specific parameters of this trap were the capture cross-section for the holes of σ_p = 10⁻¹⁶–4 × 10⁻¹⁵ cm² and the trap concentration of N_T = 3–4 × 10¹⁴ cm⁻³. PL measurements confirmed that the trap lies approximately 83–89 meV above the valence band edge and its location. Full article

According to our recent studies on hydrophobicity, this work is aimed at understanding the dependence of hydrophobic interactions on the shape of a solute’s surface. It has been observed that dissolved solutes primarily affect the structure of interfacial water, which refers to the top layer of water at the interface between the solute and water. As solutes aggregate in a solution, hydrophobic interactions become closely related to the transition of water molecules from the interfacial region to the bulk water. It is inferred that hydrophobic interactions may depend on the shape of the solute surface. To enhance the strength of hydrophobic interactions, the solutes tend to aggregate, thereby minimizing their surface area-to-volume ratio. This also suggests that hydrophobic interactions may exhibit directional characteristics. Moreover, this phenomenon can be supported by calculated potential mean forces (PMFs) using molecular dynamics (MD) simulations, where different surfaces, such as convex, flat, or concave, are associated with a sphere. Furthermore, this concept can be extended to comprehend the molecular packing parameter, commonly utilized in studying the self-assembly behavior of amphiphilic molecules in aqueous solutions. Full article

To determine the optimal salinity of irrigation water for fruit radish cultivated in peat, five levels of salinized water were used to evaluate their effect on the growth and quality of fruit radish (Raphanus sativus L.). Results showed that with an increase in salinity, the leaf growth was somehow inhibited, but the fleshy root growth increased, and quality improved with more soluble solids, sugar, protein, and Vitamin C substances in fleshy roots. With an increase in water salinity up to 4.2 dS/m, the weight of fleshy roots increased by 51.10% with a high increment in the root/shoot ratio. With the same salt concentration, the content of soluble solids in both root peal and pulp was the highest and improved by 11.06% and 6.70%, respectively. The soluble sugar content was the highest in root peals with the 4.2 dS/m treatment and in fleshy roots with the 7.4 dS/m treatment, with a 55.85% and 32.30% increase, respectively. The content of both soluble protein and vitamin C with the 4.2 dS/m treatment increased by 11.99% and 113.36%, respectively. Strong evidence derived from the study has indicated that 4.2 dS/m salinized irrigation water is optimal for growing ‘ice-cream’ fruit radishes and maintaining ultimate root weight and quality. Full article

Zinc deficiency is a common nutritional disorder with detrimental health consequences. Whether parental zinc deficiency induces intergenerational effects remains largely unknown. We investigated the effects of a combined maternal and paternal zinc deficiency on offspring’s metabolic outcomes and gene expression changes in Drosophila melanogaster. The parent flies were raised on zinc-deficient diets throughout development, and their progeny were assessed. Offspring from zinc-deprived parents exhibited a significant (p < 0.05) increase in body weight and whole-body zinc levels. They also displayed disrupted glucose metabolism, altered lipid homeostasis, and diminished activity of antioxidant enzymes. Gene expression analysis revealed significant (p < 0.05) alterations in zinc transport genes, with increases in mRNA levels of dZIP1 and dZnT1 for female and male offspring, respectively. Both sexes exhibited reduced dZnT35C mRNA levels and significant (p < 0.05) increases in the mRNA levels of DILP2 and proinflammatory markers, Eiger and UPD2. Overall, female offspring showed higher sensitivity to parental zinc deficiency. Our findings underscore zinc’s crucial role in maintaining health and the gender-specific responses to zinc deficiency. There is the need for further exploration of the underlying mechanisms behind these intergenerational effects. Full article

The emergence of construction 5.0 marks a shift toward a human-centric approach to digitization within the construction industry. Along with diverse digital innovations related to this shift, blockchain technology offers vast opportunities for the construction industry, including streamlining project management processes, enhancing transparency in payment processes, and improving contract administration. This paper systematically reviews 109 articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to examine the applications of blockchain in construction, identifying twenty-three topics across eight thematic areas. These areas were further mapped using VOSviewer Online version 1.2.3 to identify interrelationships among the themes and highlight their broad impact. Key features like immutability, security, transparency, and traceability show promise in contract administration, supply chain logistics, facilities management, and sustainability. However, the study also describes the challenges of adopting blockchain in construction, emphasizing the need for enhanced stakeholder education, improved regulatory frameworks, and the creation of industry-specific blockchain platforms to support its acceptance in the construction industry. Emphasizing emerging blockchain applications and the adoption challenges equips researchers and practitioners with the knowledge of these applications and their significance to construction practices. Full article

Understanding the modification mechanism of C₉ petroleum resin (C₉PR) on styrene–butadiene–styrene (SBS) polymer modified asphalt properties is of significant importance. In this paper, dynamic shear rheometer (DSR), storage stability, fluorescence morphology (FM), scanning electron microscope (SEM), Fourier transform-infrared (FTIR) spectrometer, and molecular dynamic (MD) simulation were adopted to evaluate the rheological, chemical, and microstructure molecular motion state of C₉PR and SBS composite modified asphalt at different aging states. The DSR storage results indicate that the addition of C₉PR could improve the high-temperature property, storage stability, and temperature susceptibility. FM and SEM results indicate that the network microstructure was enhanced and the roughness between polymer resins and virgin asphalt was improved at the microscopic scale. The MD results indicate that the heterogeneity between C₉PR and SBS modified asphalt was demonstrated, and the bonding energies were enhanced with the addition of C₉PR. Moreover, the FTIR results indicate that new function groups were generated in addition to C₉PR. In general, the addition of C₉PR is a good approach to promote high-quality polymer modified asphalt (PMA) for pavement engineering. Full article

To strengthen major transportation infrastructure projects’ (MTIPs’) organizational resilience and fortify their capacity for crisis management and project risk prevention. In this paper, based on the resilience theory development process, the connotation of organizational resilience of MTIPs is defined, and 20 influencing factors of organizational resilience of MTIPs are extracted from four categories of stability, redundancy, adaptability, and rapidity according to the literature analysis and case study method. The significance, causality, and multilevel recursive order structure of the influencing factors were investigated by the fuzzy DEMATEL-ISM approach, and their driving and dependent characteristics were analyzed through MICMAC. The results indicate that risk warning and prediction, human resources management, inter-organizational synergies, resource reserve situations, organizational leadership, and organizational learning are the crucial factors of organizational resilience in MTIPs. There are three levels and five ranks in the multilevel recursive rank structure of the factors affecting MTIPs’ organizational resilience. Among them, risk warning and prediction, equipment condition and performance, human resources management, and organizational leadership have the deepest impact on organizational resilience in MTIPs. The findings can clarify ideas for subsequent research on organizational resilience in this area and inform project decision-makers in developing strategies for optimizing organizational resilience. Full article

Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe. Full article

The growing importance of environmental efficiency in reducing carbon emissions has prompted scientists around the world to intensify their efforts to prevent the destructive effects of a changing climate and a warming planet. Global carbon emissions rose by more than 40% in 2021, leading to significant variations in the planet’s weather patterns. Nevertheless, a significant proportion of natural resources continue to be exploited. To prepare for this challenge, it is essential to implement a sustainable approach in the construction industry. Biobased materials are made primarily from renewable raw materials like hemp, straw, miscanthus, and jute. These new materials provide excellent thermal and acoustic performance and make optimum use of local natural resources such as agricultural waste. Nowadays, cement is one of the most important construction materials. In an attempt to meet this exciting challenge, biobased materials with low-carbon binders are one of the proposed solutions to create a more insulating and less polluting material. The aim of this review is to investigate and to analyze the impact of the incorporation of different types of biobased materials on the mechanical, thermal, and hygric performance of a mix using different types of binder. Full article

Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining different methods often yields better results than relying on individual techniques. The accuracy of tidal current power is very important, especially for smart grid applications. This work proposes hybrid adaptive neuro-fuzzy inference system (ANFIS) with the Kalman filter (KF) and a neuro-wavelet (WNN) for tidal current speed, direction, and power forecasting. The turbine used in this study is driven by a direct drive permanent magnet synchronous generator (DDPMSG). The predictions of individual and hybrid models including the ANFIS, the Kalman filter, and the WNN for tidal current speed and the power it generates are compared with another dataset as a way of validation which is the tidal currents direction. Also, other published work results in the literature are compared to the proposed work. Different hybrid models are proposed for smart grid integration. The results of this work indicate that the hybrid model of the WNN and the ANFIS for tidal current power or speed forecasting has the highest performance compared to all other models. Full article

Robotic manipulators are critical for industrial automation, boosting productivity, quality, and safety in various production applications. Key factors like the payload, speed, accuracy, and reach define robot performance. Optimizing these factors is crucial for future robot applications across diverse fields. While 6-Degrees-of-Freedom (DoF)-articulated robots are popular due to their diverse applications, this research proposes a novel 5-DoF robot design for industrial automation, featuring a combination of three prismatic and two revolute (2R) joints, and analyzes its workspace. The proposed techno-economically efficient design offers control over the robot manipulator to achieve any reachable position and orientation within its workspace, replacing traditional 6-DoF robots. The kinematic model integrates both parallel and serial manipulator principles, combining a Cartesian mechanism with rotational mechanisms. Simulations demonstrate the end effector’s flexibility for tasks like welding, additive manufacturing, and material inspections, achieving the desired position and orientation. The research encompasses the design of linear and rotational actuators, kinematic modeling, Human–Machine Interface (HMI) development, and welding application integration. The developed robot demonstrates a superior performance and user-friendliness in welding. The experimental work validates the design’s optimized joint trajectories, efficient power usage, singularity avoidance, easy access in application areas, and reduced costs due to fewer actuators. Full article

The aim of the present research was to determine if the developed ovo−vegetarian sausage (SO), which was made with 15% chickpea flour, 51% albumin and 34% soy protein concentrate, exhibited improved physicochemical and sensory characteristics compared to vegetarian sausages available on the local market (classic vegan sausage, SC; vegan fine herb sausage, SH; and quinoa sausage, SQ). According to the physicochemical results, the developed sample, SO, presented significant differences (p < 0.05) compared to the others, including higher protein content, lower pH and a higher a* value. Three types of sensory analyses were conducted—flash profile, overall liking and purchase intention (to determine consumers’ willingness to purchase the product)—with the first involving 15 consumers and the second and third involving 60 participants each. Descriptors for each sample were determined using the vocabulary provided by consumers in the flash profile analysis. Descriptors for SO included ‘elastic’, ‘smell of cooked corn’, ‘characteristic flavor’, ‘pasty’, ‘soft’ and ‘pastel color’, contributing to its greater overall liking and purchase intention compared to the others. Through the hierarchical multiple factor analysis, a positive correlation was observed between the texture and sensory descriptors of the flash profile. Conversely, a correlation was found between the physicochemical characteristics (pH, a_w, color) and overall liking and purchase intention. Full article

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. Full article

Saffron (Crocus sativus) floral by-products are a source of phenolic compounds that can be recovered and used in the nutraceutical, pharmaceutical, or cosmetic industries. This study aimed to evaluate the phenolic compounds’ extraction using green extraction techniques (GETs) in saffron floral by-products and to explore the influence of selected extraction techniques on the phytochemical composition of the extracts. Specifically, ultrasound-assisted extraction (UAE), subcritical water extraction (SWE), and deep eutectic solvents extraction (DESE) were used. Phenolic compounds were identified with (HR) LC-ESI-QTOF MS/MS analysis, and the quantitative analysis was performed with HPLC-PDA. Concerning the extraction techniques, UAE showed the highest amount for both anthocyanins and flavonoids with 50:50% v/v ethanol/water as solvent (93.43 ± 4.67 mg/g of dry plant, dp). Among SWE, extraction with 96% ethanol and t = 125 °C gave the best quantitative results. The 16 different solvent mixtures used for the DESE showed the highest amount of flavonoids (110.95 ± 5.55–73.25 ± 3.66 mg/g dp), while anthocyanins were better extracted with choline chloride:butane-1,4-diol (16.0 ± 0.80 mg/g dp). Consequently, GETs can be employed to extract the bioactive compounds from saffron floral by-products, implementing recycling and reduction of waste and fitting into the broader circular economy discussion. Full article

Background: We aimed to assess medication risks and determine factors influencing the health-related quality of life (HRQOL) in cancer inpatients. Methods: A retrospective analysis was conducted to identify drug-related problems (DRPs) based on medication reviews, including patient-reported outcomes (PROs). Multiple linear regression analyses were performed to identify sociodemographic, disease-related, and drug therapy-related factors influencing changes from hospital admission to discharge in the scales of the EORTC QLQ-C30 questionnaire. Results: A total of 162 inpatients with various hematological and solid cancer diseases was analyzed. Patients received a mean of 11.6 drugs and 92.6% of patients exhibited polymedication resulting in a mean of 4.0 DRPs per patient. Based on PRO data, 21.5% of DRPs were identified. Multiple linear regression models described the variance of the changes in global HRQOL and physical function in a weak-to-moderate way. While drug therapy-related factors had no influence, relapse status and duration of hospital stay were identified as significant covariates for global HRQOL and physical function, respectively. Conclusion: This analysis describes underlying DRPs in a German cancer inpatient population. PROs provided valuable information for performing medication reviews. The multiple linear regression models for global HRQOL and physical function provided explanations for changes during hospital stay. Full article

Background: Both cervical cancer and cervical intraepithelial neoplasia (CIN) are associated with human papillomavirus (HPV) infection at different anogenital sites, but the infection features of high-risk (HR) HPVs at these sites and their association with cervical lesions have not been well characterized. Given the limitation of cervical HPV 16/18 test in screening patients with high-grade CIN (CIN 2+), studies on whether non-16/18 HR-HPV subtype(s) have potential as additional indicator(s) to improve CIN 2+ screening are needed. Methods: The infection of 15 HR-HPVs in vulva, anus, vagina, and cervix of 499 Chinese women was analyzed, and CIN lesion-associated HR-HPV subtypes were revealed. Results: In addition to the well-known cervical-cancer-associated HPV 16, 52, and 58, HPV 51, 53, and 56 were also identified as high-frequency detected subtypes prevalently and consistently present at the anogenital sites studied, preferentially in multi-infection patterns. HPV 16, 52, 58, 56, and 53 were the top five prevalent subtypes in patients with CIN 2+. In addition, we found that cervical HPV 33/35/52/53/56/58 co-testing with HPV 16/18 might improve CIN 2+ screening performance. Conclusion: This study provided a new insight into HR-HPV screening strategy based on different subtype combinations, which might be used in risk stratification clinically. Full article