Smallholder farmers in Malawi largely depend on rainfed agriculture, despite climate change posing serious threats to this form of agriculture. The adoption of climate-smart agricultural practices is pivotal in offsetting the effects of climate change on agriculture. Despite the great potential of climate-smart agricultural practices in combating the effects of climate change on agriculture, smallholder farmers’ adoption of it remains low. Reasons are varied, suggesting that the factors are largely contextual. This study, therefore, explored the determinants of smallholder farmers’ adoption of climate-smart agricultural practices in Zomba, within the framework of the diffusion of innovations. Using a convergent mixed research design, the study purposively selected key informants and randomly selected smallholder farmers. Questionnaires, interviews, and observation guides were used to collect data. Thematic analysis was used to analyse qualitative data, while descriptive analysis was used to analyse quantitative data. Results indicate that only 26% of smallholder farmers have adopted the promoted climate-smart agricultural practices. Compatibility and simplicity are the chief determinants of climate-smart agricultural practice adoption. The majority of smallholder farmers fall under the late majority. Evidence shows that labour and capital intensive climate-smart agricultural practices are less likely to be adopted by smallholder farmers. Implementing agencies, therefore, should first consult the local farmers on the ground to see practices that are already being followed and are suitable before bringing the new ones. This will ensure that the appropriate climate-smart agricultural practices are promoted. Full article

This article is about a framework for determining the degree of realism of any given passenger ride motion discomfort measurement formula. After providing some context and reviewing evidence of deficiency in currently popular ride motion discomfort measurement formulas, the article outlines the research program that needs to be carried out in order to establish such a framework. The research begins with gathering recordings of uncomfortable ride motion episodes encountered in a chosen type of passenger transport service. It then has test subjects compare the episodes via a ride motion simulator and adjust their amplitudes pair wise until they cause equal discomfort. It explains how to take the pair wise amplitude adjustments and determine amplitude adjustments that bring all of the motion episode recordings to a common level of discomfort so that they form a normalized set. Then, the lower the scatter of the scores assigned by any given discomfort measurement formula to the members of that set, the more realistic that formula will be for the chosen service. Full article

The propagation of Crocus sativus L. relies exclusively on corm multiplication. As underground storage organs, corms are susceptible to a wide range of pathogens, environmental stresses, and diseases, making traditional propagation methods often ineffective with the loss of valuable material. In vitro propagation offers an alternative for the saffron culture under controlled conditions. In particular, the innovative application of the Temporary Immersion System (TIS) represents a technological advancement for enhancing biomass production with a reduction in operational costs. The current study utilized the Plantform™ bioreactor to propagate in vitro saffron corms from the ‘Abruzzo’ region (Italy), integrating machine learning models to assess its performance. The evaluation of saffron explants after 30, 60, and 90 days of culture showed a marked improvement in growth and microcorm production compared to conventional in vitro culture on semisolid medium, supported by the machine learning analysis. Indeed, the Random Forest algorithm revealed a predictive accuracy with an R² value of 0.81 for microcorm number, showcasing the capability of machine learning models to forecast propagation outcomes effectively. These results confirm that applying TIS in saffron culture could lead to economically viable, large biomass production within a controlled environment, irrespective of seasonality. This study represents the first endeavor to use TIS technology to enhance the in vitro propagation of saffron in conjunction with machine learning, suggesting an innovative approach for cultivating high-value crops like saffron. Full article

(1) Background: This study investigated the effects of sequenced electromagnetic fields, modulated at extremely low frequencies and intensities, in the treatment of drug-resistant Escherichia coli (E. coli)-induced chronic bacterial cystitis. (2) Methods: A total of 148 female participants, aged 18 to 80 years diagnosed with chronic bacterial cystitis caused by drug-resistant E. coli, were recruited for this study. Participants were randomly assigned to two groups: an experimental group (n = 74) with osteopathic palpation and assessment treated with a sequence of electromagnetic fields, and a control group (n = 74) receiving a placebo treatment. Both groups were assessed at this study’s outset, 4 weeks after eight applications, and at 12 weeks for symptomatic presentation and laboratory parameters. (3) Results: After 4 weeks of treatment, a significant difference was observed between the two groups regarding D-DIMER levels, IL-6 levels, erythrocyte levels, leukocyte levels, and E. coli levels (p < 0.001). By the 12th week, the experimental group continued to exhibit a significant reduction in the examined parameters compared to the control group (p < 0.001). Additionally, the treatment did not induce any side effects in the patients in the experimental group. (4) Conclusions: Treatment with coherently sequenced electromagnetic fields, modulated at an extremely low frequency and intensity, not only appears to provide an effective alternative for the symptoms of chronic bacterial cystitis caused by drug-resistant E. coli but also demonstrates a potent antibacterial effect. Full article

Wildfires present a significant risk to societies globally due to population growth, concentrated activities, climate change, and extreme environmental conditions. To establish effective fire suppression and management policies, it is crucial to determine whether the distance between ignition points and urban areas is increasing or decreasing. This study analyzes 101,597 fires in Spain and California between 2007 and 2015, where ignition points and all built-up areas were precisely geolocated in 2014. The study employs the Mann–Kendall test to determine trends and analyze the relative distance of wildfires to human buildings over time. The results indicate that wildfires are becoming statistically significantly closer in several areas. It is also observed that the majority of wildfires are moving away from buildings, although this is not a significant finding. These results suggest the possibility of further research into the reasons behind these observations and possible future developments and their consequences. Full article

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R²) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage. Full article

With the rapid development of the emerging intelligent, flexible, transparent, and wearable electronic devices, such as quantum-dot-based micro light-emitting diodes (micro-LEDs), thin-film transistors (TFTs), and flexible sensors, numerous pixel-level printing technologies have emerged. Among them, inkjet printing has proven to be a useful and effective tool for consistently printing micron-level ink droplets, for instance, smaller than 50 µm, onto wearable electronic devices. However, quickly and accurately determining the printing quality, which is significant for the electronic device performance, is challenging due to the large quantity and micron size of ink droplets. Therefore, leveraging existing image processing algorithms, we have developed an effective method and software for quickly detecting the morphology of printed inks served in inkjet printing. This method is based on the edge detection technology. We believe this method can greatly meet the increasing demands for quick evaluation of print quality in inkjet printing. Full article

In this paper, we introduce a novel rate control scheme specifically tailored for live broadcasting scenarios. Notably, in high-definition live transmissions of sports events and video game competitions that typically exceed 1080 p resolution and run at frame rates of 60 fps or higher, the transcoding speed of encoders often becomes a limiting factor, leading to streams with substantial bitrates but unsatisfactory quality metrics. To enhance the overall Quality of Service (QoS) without increasing the bitrate, it is essential to improve the quality of Regions of Interest (ROI).Our proposed solution presents an ROI-based rate reservoir model that ingeniously leverages Convolutional Neural Networks (CNNs) to predict rate control parameters. This approach aims to optimize the bitrate allocation within high bitrate live broadcasts, thus enhancing the image quality within ROIs. Experimental outcomes demonstrate that this algorithm manages to increase the bitrate by no more than 5%, effectively redistributing the reduced bitrate across the entire Group of Pictures (GOP). As a result, it ensures a gradual decrease in the quality of Regions of Uninterest (ROU), thereby maintaining a balanced quality experience throughout the broadcasted content. Full article

The judiciary is a field lacking research in relation to its administration and innovation; however, different theoretical perspectives can be followed. This work reviews this trend while adding to it. An institutional perspective is presented, as is its explanatory potential. This perspective captures the context of the public sector; however, when analyzing its interpretation in terms of innovation, it is revealed to be doubly paradoxical. From the theoretical point of view, institutionalization focuses on the maintenance of processes, while innovation, gradually or abruptly, investigates their disruptions. Nevertheless, institutionalization can be observed as part of the sedimentation of innovation. Institutionalization is presented, in the context of innovation, as a selection mechanism that shapes such innovation. This paradox is presented under the review of organizational institutionalism vis-à-vis innovation and, for its unfolding, considers the adoption of innovation as an adaptation to the prevalent rationalized elements. This presentation is paralleled with the interpretation that innovation is limited by a structure that, sometimes rationalized, forms its trajectory. Considering the social function of the judiciary that is anchored in institutionalism, historical institutionalism is thus added, centrally placing the judiciary in the current institutional matrix and associating its path dependence with the dimensions of its innovation. Based on these outlines, propositions and a suggested agenda for future research are presented. Full article

Background and Aims: Accurate recognition of endoscopic instruments facilitates quantitative evaluation and quality control of endoscopic procedures. However, no relevant research has been reported. In this study, we aimed to develop a computer-assisted system, EndoAdd, for automated endoscopic surgical video analysis based on our dataset of endoscopic instrument images. Methods: Large training and validation datasets containing 45,143 images of 10 different endoscopic instruments and a test dataset of 18,375 images collected from several medical centers were used in this research. Annotated image frames were used to train the state-of-the-art object detection model, YOLO-v5, to identify the instruments. Based on the frame-level prediction results, we further developed a hidden Markov model to perform video analysis and generate heatmaps to summarize the videos. Results: EndoAdd achieved high accuracy (>97%) on the test dataset for all 10 endoscopic instrument types. The mean average accuracy, precision, recall, and F1-score were 99.1%, 92.0%, 88.8%, and 89.3%, respectively. The area under the curve values exceeded 0.94 for all instrument types. Heatmaps of endoscopic procedures were generated for both retrospective and real-time analyses. Conclusions: We successfully developed an automated endoscopic video analysis system, EndoAdd, which supports retrospective assessment and real-time monitoring. It can be used for data analysis and quality control of endoscopic procedures in clinical practice. Full article

Residual film pollution and excessive nitrogen fertilizer have become limiting factors for agricultural development. To investigate the feasibility of replacing conventional plastic film with biodegradable plastic film in cold and arid environments under nitrogen application conditions, field experiments were conducted from 2021 to 2022 with plastic film covering (including degradable plastic film (D) and ordinary plastic film (P)) combined with nitrogen fertilizer 0 (N0), 160 (N1), 320 (N2), and 480 (N3) kg·ha⁻¹. The results showed no significant difference (p > 0.05) in dry matter accumulation, photosynthetic gas exchange parameters, soil enzyme activity, or yield of spring maize under degradable plastic film cover compared to ordinary plastic film cover. Nitrogen fertilizer is the main factor limiting the growth of spring maize. The above-ground and root biomass showed a trend of increasing and then decreasing with the increase in nitrogen application level. Increasing nitrogen fertilizer can also improve the photosynthetic gas exchange parameters of leaves, maintain soil enzyme activity, and reduce soil pH. Under the nitrogen application level of N2, the yield of degradable plastic film and ordinary plastic film coverage increased by 3.74~42.50% and 2.05~40.02%, respectively. At the same time, it can also improve water use efficiency and irrigation water use efficiency, but it will reduce nitrogen fertilizer partial productivity and nitrogen fertilizer agronomic use efficiency. Using multiple indicators to evaluate the effect of plastic film mulching combined with nitrogen fertilizer on the comprehensive growth of spring maize, it was found that the DN2 treatment had the best complete growth of maize, which was the best model for achieving stable yield and income increase and green development of spring maize in cold and cool irrigation areas. Full article

Nanosilver is a popular nanomaterial, the potential influence of which on humans is of serious concern. Herein, we exposed male Wistar rats to two regimens: a repeated oral dose of 30 mg/kg bw silver nanoparticles (AgNPs) over 28 days and a single-dose injection of 5 mg/kg bw of AgNPs. At three different time points, we assessed antioxidant defense, oxidative stress and inflammatory parameters in the colon, as well as toxicity markers in the liver and plasma. Both experimental scenarios showed increased oxidative stress and inflammation in the colon. Oral administration seemed to be linked to increased reactive oxygen species generation and lipid peroxidation, while the effects induced by the intravenous exposure were probably mediated by silver ions released from the AgNPs. Repeated oral exposure had a more detrimental effect than the single-dose injection. In conclusion, both administration routes had a similar impact on the colon, although the underlying mechanisms are likely different. Full article

Vibrio cholerae is the etiological agent of cholera in humans. The bacterium is frequently detected in aquatic products worldwide. However, the current literature on the genome evolution of V. cholerae of aquatic animal origins is limited. Here, we firstly characterized the growth and genome features of V. cholerae isolates with different resistance phenotypes from three species of common freshwater fish. The results revealed that the non-O1/O139 V. cholerae isolates (n = 4) were halophilic and grew optimally at 2% NaCl and pH 8.0. Their draft genome sequences were 3.89 Mb–4.15 Mb with an average GC content of 47.35–47.63%. Approximately 3366–3561 genes were predicted to encode proteins, but 14.9–17.3% of them were of an unknown function. A number of strain-specific genes (n = 221–311) were found in the four V. cholerae isolates, 3 of which belonged to none of any of the known sequence types (STs). Several putative mobile genetic elements (MGEs) existed in the V. cholerae isolates, including genomic islands (n = 4–9), prophages (n = 0–3), integrons (n = 1–1), and insertion sequences (n = 0–3). Notably, CRISPR-Cas system arrays (n = 2–10) were found in the V. cholerae genomes, whereby the potential immunity defense system could be active. Comparative genomic analyses also revealed many putative virulence-associated genes (n = 106–122) and antibiotic resistance-related genes (n = 6–9). Overall, the results of this study demonstrate the bacterial broader-spectrum growth traits and fill prior gaps in the genomes of V. cholerae originating from freshwater fish. Full article

This study aimed to investigate how stakeholders in the United States beef industry incorporate animal welfare into their sustainability programs. A survey was administered online to the U.S. Roundtable for Sustainable Beef membership. Twenty-seven surveys were analyzed. Most respondents (n = 26, 96%) had sustainability programs that incorporated animal welfare. Most respondents believed that welfare positively impacted environmental (n = 25/26, 96%), economic (n = 25/26, 96%), and social (n = 26/26, 100%) sustainability. The thematic analysis of five free response questions identified ten themes: Animal Care, Regulations and Guidelines, Responsibility, Consumers and Stakeholders, Performance and Efficiency, Financial Impact, Connectedness, Critical Component, Animal-based Outcomes, and Employees. When asked to define welfare, the most common themes were Animal Care and Regulations and Guidelines. When asked why welfare was a component of their sustainability program, the top factors from a provided list were: cattle health (n = 20, 74%), cattle performance (n = 12, 44%), and consumer perceptions (n = 12, 44%). Findings suggest a widespread recognition of animal welfare’s importance within sustainable beef production. Full article

One of the crucial factors in grain storage is appropriate moisture content, which plays a significant role in reducing storage losses and ensuring quality. However, currently available humidity sensors on the market fail to meet the demands of modern large-scale grain storage in China in terms of price, size, and ease of implementation. Therefore, this study aims to develop an economical, efficient, and easily deployable grain humidity sensor suitable for large-scale grain storage environments. Simultaneously, it constructs humidity calibration models applicable to three major grain crops: millet, rice, and wheat. Starting with the probe structure, this study analyzes the ideal probe structure for grain humidity sensors. Experimental validations are conducted using millet, rice, and wheat as experimental subjects to verify the accuracy of the sensor and humidity calibration models. The experimental results indicate that the optimal length of the probe under ideal conditions is 0.67 m. Humidity calibration models for millet, rice, and wheat are constructed using SVM models, with all three models achieving a correlation coefficient R² greater than 0.9. The measured data and model-calculated data show a linear relationship, closely approximating y = x, with R² values of all three fitted models above 0.9. In conclusion, this study provides reliable sensor technological support for humidity monitoring in large-scale grain storage and processing, with extensive applications in grain storage and grain safety management. Full article

Haptic feedback holds the potential to enhance the engagement and expressivity of future digital and electric musical instruments. This study investigates the impact of artificial vibration on the perceived quality of a silent electric cello. We developed a haptic cello prototype capable of rendering vibration signals of varying degree of congruence with the produced sound. Experienced cellists participated in an experiment comparing setups with and without vibrotactile feedback, rating them on preference, perceived power, liveliness, and feel. Results show nuanced effects, with added vibrations moderately enhancing feel and liveliness, and significantly increasing perceived power when using vibrations obtained from the pickup at the cello’s bridge. High uncertainty in our statistical model parameters underscores substantial individual differences in the participants responses, as commonly found in qualitative assessments, and highlights the importance of consistent feedback in the vibrotactile and auditory channels. Our findings contribute valuable insights to the intersection of haptics and music technology, paving the way for creating richer and more engaging experiences with future musical instruments. Full article

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results. Full article

Despite a continuous effort devoted by the scientific community, a large-scale employment of Pulsating Heat Pipes for thermal management applications is still nowadays undermined by the low reliability of such heat transfer systems. The main reason underlying this critical issue is linked to the strongly chaotic thermofluidic behavior of these devices, which prevents a robust prediction of their working behavior for different geometries and operating conditions, consequently hampering proper industrial design. The present work proposes to thoroughly compare data referring to previous infrared investigations on different Pulsating Heat Pipe layouts, which have focused on the estimation of heat fluxes locally exchanged at the wall–fluid interfaces. The aim is to understand the beneficial contribution of local heat transfer quantities in the prediction of the complex physics underlying such heat transfer systems. The results have highlighted that, regardless of the considered geometry and working conditions, wall-to-fluid heat fluxes are able to provide useful quantities to be employed, to some extent, to generalize Pulsating Heat Pipe operation and to improve their existing numerical models. Full article

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP. Full article

Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms traditional methods, on average. The proposed methodology is more in line with the needs of practical operation in an environment full of uncertainty. Full article

Background: The natural history of spinal muscular atrophy (SMA) is well understood, with progressive muscle weakness resulting in declines in function. The development of contractures is common and negatively impacts function. Clinically, joint hypermobility (JH) is observed but is poorly described, and its relationship with function is unknown. Methods: Lower-limb ROM (range of motion) assessments of extension and flexion at the hip, knee, and ankle were performed. ROMs exceeding the published norms were included in the analysis. The functional assessments performed included the six-minute walk test (6 MWT) and the Hammersmith Functional Motor Scale—Expanded (HFMSE). Results: Of the 143 participants, 86% (n = 123) had at least one ROM measure that was hypermobile, and 22% (n = 32) had three or more. The HFMSE scores were inversely correlated with hip extension JH (r = −0.60, p = 0.21; n = 6) and positively correlated with knee flexion JH (r = 0.24, p = 0.02, n = 89). There was a moderate, inverse relationship between the 6 MWT distance and ankle plantar flexion JH (r = −0.73, p = 0.002; n = 15). Conclusions: JH was identified in nearly all participants in at least one joint in this study. Hip extension, knee flexion and ankle plantar flexion JH was associated with function. A further understanding of the trajectory of lower-limb joint ROM is needed to improve future rehabilitation strategies. Full article

We propose an efficient scheme to enhance the generation of optical second-order sidebands (OSSs) in an atom-assisted optomechanical system. The cavity field is coupled with a strong driving field and a weak probe field, and a control field is applied to the atom. We use the steady-state method to analyze the nonlinear interaction in the system, which is different from the traditional linear analysis method. The existence of an auxiliary three-level atom driven by the control field significantly enhances the generation of an OSS. It is found that the efficiency of the OSS can be effectively modulated by adjusting the Rabi frequency of the control field, optomechanical cooperativity and atomic coupling strength. Our scheme provides a promising solution for controlling light propagation and has potential application in quantum optical devices and quantum information networks. Full article