Yang Luo, Anwar P. P. Abdul Majeed, Zaid Omar
et al.
Accurate grading of Oil Palm Fresh Fruit Bunches (FFB) is pivotal for maximizing agricultural yield, yet manual assessment in unstructured environments remains labor-intensive and subjective. While Convolutional Neural Networks (CNNs) offer an automated solution, the conventional strategy of scaling network depth often yields diminishing returns or overfitting on moderately sized datasets. To overcome these limitations, this study proposes the Deep Feature Concatenation (DFC) framework. Rather than deepening a single architecture, this methodology synergizes the spatial hierarchy preservation of ResNet50 with the dense feature-reuse mechanisms of DenseNet121. This fusion creates a composite representation space that captures complementary inductive biases. To ensure computational efficiency, the framework decouples representation learning from inference. Principal Component Analysis (PCA) retains 99% of explained variance while compressing features by 68%. These optimized representations are classified using shallow linear probes. Validated on a single-source dataset expanded to 4000 images (derived from 466 original samples) using a rigorous “Parent–Child” split to prevent data leakage, DFC achieved a peak accuracy of 97.75%. McNemar’s statistical test indicated that this performance outperforms the ResNet50 baseline (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>=</mo><mn>0.039</mn></mrow></semantics></math></inline-formula>) for SVM classifiers. However, it is critical to note that these results represent a proof of concept based on a limited biological sample size, particularly for rare defect classes. While the model achieved 100% detection accuracy for critical defects within the specific validation set, the high synthetic-to-original ratio necessitates cautious interpretation regarding external validity. This framework provides a practical foundation for future research into high-precision, low-latency grading systems, but multi-center validation on larger, independent datasets is required to confirm broad generalizability across diverse plantation environments.
Misbah Bibi, Muhammad Faseeh, Anam Nawaz Khan
et al.
Existing object detection and annotation methods in surveillance systems often suffer from inefficiencies due to manual labeling and a lack of accurate distance estimation, which limits their effectiveness in large-scale environments. These limitations reduce the speed and accuracy required for real-time surveillance, especially in scenarios that necessitate simultaneous monitoring of multiple feeds. To address these challenges, this paper proposes a framework for automated object detection and annotation, specifically designed for surveillance applications. The framework incorporates both manual and automatic modes, offering flexibility in object labeling. A synthetic data is created by using the blender tool which emulates the real-world security and surveillance environments, is utilized to train a model for fast and accurate object recognition and identification. Moreover, for precise distance estimation a depth estimation model is used for calculating the distance for detected objects. The proposed architecture presents both manual and auto modes for the object detection by incorporating the both models in proposed framework. The auto mode of the proposed architecture increases the efficiency of the large surveillance and monitoring applications while lowering the manual labor. The model’s performance is evaluated with state-of-the-art models to assess its performance for auto detection and recognition. The precision and recall above 90% shows that our fine-tuned model demonstrates improved results on synthetic data. With real time surveillance and risk assessment as the foundation, this method seamlessly overthrows drawbacks of existing methods.
College counselors around the world, as pivotal components of educational support systems, provide substantial psychological and emotional counseling, making them high-intensity emotional workers. In China, college counselors, who are the key force in ideological and political education, possess unique professional characteristics and conflicting job demands. Such persistent emotional pressure often induces emotional exhaustion, a primary driver of their high turnover rates. Therefore, this paper explores how emotional labor impacts counselors’ turnover intention, examining the mediating role of emotional exhaustion and the moderating role of perceived organizational support through a survey of 337 counselors. The findings indicate: Deep acting and perceived organizational support correlate negatively with turnover intention, while surface acting and emotional exhaustion correlate positively. Emotional exhaustion partially mediates the link between surface acting and turnover intention, and fully mediates the relationship between deep acting and turnover intention. Additionally, perceived organizational support strengthens the mediating effect of emotional exhaustion in the deep acting pathway and weakens it in the surface acting pathway. This paper theoretically extends emotional labor theory to hybrid roles and practically informs counselor team stability and “de-administration” reforms in China. Its insights into “role conflict-induced emotional regulation dynamics”, can be generalized to all professions that require a balance between conflicting role demands, enriching cross-cultural emotional labor research.
Heily Consepción Portocarrero Ramos, Jonathan Alberto Campos Trigoso, Omer Cruz Caro
et al.
Employment outcomes are more strongly associated with specific career paths than with academic performance. Despite expanding university access, significant gaps persist between the training received and labor market conditions. The objective was to identify and analyze the factors that influence the employment situation of university graduates. A quantitative explanatory approach was used, with a sample of 3,009 graduates. A structured survey was administered, and the data were analyzed using Logistic regression, Lasso regression, and Random Forest models. The results show that the variables with the greatest predictive power are the type of contract, time spent working, and income level. In contrast, academic variables such as GPA and theoretical or practical training showed little relevance. In comparison, employability is more associated with specific career paths than academic merits. The study reveals important findings for universities to strengthen applied training, encourage early entry into the workforce, and develop monitoring systems that allow them to adapt their educational offerings to the real demands of the professional environment. Understanding the factors that influence graduate employability is crucial to enhancing the significance of education and improving professional opportunities.
Many forms of traditional raised bed systems could be found around the world. Several of them have been identified as Globally Important Agricultural Heritage Systems (GIAHS) sites by Food and Agriculture Organization of the United Nations. Unlike traditional raised bed systems with similar structures in the Americas, the high bed–low ditch (HBLD) system in tropical and subtropical Asia, which is originated and developed from rice production, has been playing an important role in enhancing food security and maintaining farmer livelihood for centuries. Moreover, products provided by HBLD system are not only important for the livelihood of the local farmers, but also important for people living in the nearby towns and cities especially for vegetable and fruit supply. In this system, the ditches or sunken beds can be used to lower the groundwater table, retain nutrients and soil particles washed from the bed, grow rice or aquatic vegetables, and raise fish or shrimp. The HBLD system can also help to reduce salinity in coastal lowlands due to the presence of ditches. The raised beds can be used to grow various upland crops. Compared with rice monocropping, the adoption of HBLD system significantly improves the cropping intensity, productivity, employment, and income of farmers. Famers’ long-term practices fully demonstrate that this system is a type of sustainable agriculture with strong adaptability to the changes of natural environment. However, it should also be noted that the large-scale development of HBLD systems is not simply dependent on natural conditions, but is also determined by specific socioeconomic factors, such as good transportation facilities, a well market system, and a sufficient supply of labor. As a model of equilibrium between food production and high levels of biodiversity maintenance, the HBLD system is an important agricultural heritage system with global significance, and it should be well preserved and utilized in new ways to realize its important multiple functions under conditions of rapid urbanization in lowland and coastal regions.
Nutrition. Foods and food supply, Food processing and manufacture
Rajmeet Singh, Asim Khan, Lakmal Seneviratne
et al.
Abstract In recent years, significant advancements have been made in the field of smart greenhouses, particularly in the application of computer vision and robotics for pollinating flowers. Robotic pollination offers several benefits, including reduced labor requirements and preservation of costly pollen through artificial tomato pollination. However, previous studies have primarily focused on the labeling and detection of tomato flowers alone. Therefore, the objective of this study was to develop a comprehensive methodology for simultaneously labeling, training, and detecting tomato flowers specifically tailored for robotic pollination. To achieve this, transfer learning techniques were employed using well-known models, namely YOLOv5 and the recently introduced YOLOv8, for tomato flower detection. The performance of both models was evaluated using the same image dataset, and a comparison was made based on their Average Precision (AP) scores to determine the superior model. The results indicated that YOLOv8 achieved a higher mean AP (mAP) of 92.6% in tomato flower and bud detection, outperforming YOLOv5 with 91.2%. Notably, YOLOv8 also demonstrated an inference speed of 0.7 ms when considering an image size of $$1920 \times 1080$$ 1920 × 1080 pixels resized to $$640 \times 640$$ 640 × 640 pixels during detection. The image dataset was acquired during both morning and evening periods to minimize the impact of lighting conditions on the detection model. These findings highlight the potential of YOLOv8 for real-time detection of tomato flowers and buds, enabling further estimation of flower blooming peaks and facilitating robotic pollination. In the context of robotic pollination, the study also focuses on the deployment of the proposed detection model on the 3P2R gantry robot. The study introduces a kinematic model and a modified circuit for the gantry robot. The position-based visual servoing method is employed to approach the detected flower during the pollination process. The effectiveness of the proposed visual servoing approach is validated in both un-clustered and clustered plant environments in the laboratory setting. Additionally, this study provides valuable theoretical and practical insights for specialists in the field of greenhouse systems, particularly in the design of flower detection algorithms using computer vision and its deployment in robotic systems used in greenhouses.
Gustavo José Querino Vasconcelos, Gabriel Schubert Ruiz Costa, Thiago Vallin Spina
et al.
More sustainable technologies in agriculture are important not only for increasing crop yields, but also for reducing the use of agrochemicals and improving energy efficiency. Recent advances rely on computer vision systems that differentiate between crops, weeds, and soil. However, manual dataset capture and annotation is labor-intensive, expensive, and time-consuming. Agricultural robots provide many benefits in effectively performing repetitive tasks faster and more accurately than humans, and despite the many advantages of using robots in agriculture, the solutions are still often expensive. In this work, we designed and built a low-cost autonomous robot (DARob) in order to facilitate image acquisition in agricultural fields. The total cost to build the robot was estimated to be around <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>$</mi><mn>850</mn></mrow></semantics></math></inline-formula>. A low-cost robot to capture datasets in agriculture offers advantages such as affordability, efficiency, accuracy, security, and access to remote areas. Furthermore, we created a new dataset for the segmentation of plants and weeds in bean crops. In total, 228 RGB images with a resolution of 704 × 480 pixels were annotated containing 75.10% soil area, 17.30% crop area and 7.58% weed area. The benchmark results were provided by training the dataset using four different deep learning segmentation models.
IntroductionOne of the major challenges that persons with disabilities (PWDs) are facing globally is unemployment. The challenge is attributed to systems that are not built with inclusivity in mind by employers. As such, the work of inclusion is not inviting PWDs to do more but to make a difference through social support. Most research on inclusion in the employment of PWDs in low-income settings has been concentrated upon the labor “supply” side, and to the best of our knowledge, no specific studies moved toward inclusion in employment issues from the employers’ perspective in informal settlements. Notably, our research question is: “what would it take to socially support employed PWD in informal settlements building from the perspectives of employers.”MethodsThis paper used data from in-depth interviews with 38 service providers in the education, health, water, sanitation, and solid waste management sectors and two sub-county officials in two informal settlements in Nairobi, Kenya. The service providers were employers or entrepreneurs who had hired PWDs in their workspaces and the sub-county officials that had vast experiences with employed PWDs. Data from transcripts were analyzed by the research team using content analysis.ResultsThe social support offered to employed PWDs included listening to them with a concern; identifying their strengths and obstacles; planning for them based on their qualities, knowledge, and experience and linking them with existing opportunities; creating specific opportunities and facilitating their access to opportunities; gradual withdrawal of support by support group; and, lastly, compromise by employers with PWD inclusion strategies. Study participants described how misdirected and inadequate resources, dissatisfaction and unhappiness, and conflicts at the workplace associated with non-inclusion were constraints to social support. Employment matters affecting PWDs are complex and require multi-pronged context-specific social support approaches. Essential to the functioning of an inclusive workplace for PWDs were communication, coordination, sharing of the workload, and supporting individual PWD.ConclusionInclusion of PWDs in the labor market is about generating a supportive workplace where people are valued and appreciated without judgement for what they can contribute. Notably, in the absence of jobs for everyone and high unemployment rates among every segment of the population, there is a need for an awareness creation, mobilization, and sensitization of employers and investors around the competencies of PWDs and their need to socially support on an impartial basis. On the other hand, employment centers could establish stations in low-income areas to advise and support PWDs on career opportunities that are disability-friendly and partner with employers to avail information about the capabilities of PWDs. Conversely, the government should provide some tax-related benefits to employers to upsurge employer incentives for hiring PWDs and empower employers on benefits and positive culture of employing PWDs. At all times, employers should be hands-on and involve diverse stakeholders to implement current policies and frameworks in different work contexts across the country and beyond.
New regimes of labor and mobility control are taking shape across the global north in a militarized form that mimics South Africa’s history of apartheid. Apartheid was a South African system of influx and labor control that attempted to manage the “threat” posed by black people by incarcerating them in zones of containment while also enabling the control and policed exploitation of black people as workers, on which the country was dependent. The paper argues, first, that the rise of a system of global apartheid has created a racialized world order and a hierarchical labor market dependent on differential access to mobility; second, that the expansion of systems of resource plunder primarily by agents of the global north into the global south renders localities in the global south unsustainable for ordinary life; and, third, that in response, the global north is massively investing in militarized border regimes to manage the northern movement of people from the global south. The paper argues that “global apartheid” might replace terms such as “transnationalism,” “multiculturalism,” and “cosmopolitanism” in order to name the structures of control that securitize the north and foster violence in the south, that gate the north and imprison the south, and that create a new militarized form of apartheid on a global level.
Pruning is one of the most important tree fruit production activities, which is highly dependent on human labor. Skilled labor is in short supply, and the increasing cost of labor is becoming a big issue for the tree fruit industry. Meanwhile, worker safety is another issue in the manual pruning. Growers are motivated to seek mechanical or robotic solutions for reducing the amount of hand labor required for pruning. Identifying tree branches/canopies with sensors as well as automated operating pruning activity are the important components in the automated pruning system. This paper reviews the research and development of sensing and automated systems for branch pruning in apple production. Tree training systems, pruning strategies, 3D structure reconstruction of tree branches, and practice mechanisms or robotics are some of the developments that need to be addressed for an effective tree branch pruning system. Our study summarizes the potential opportunities for automatic pruning with machine-friendly modern tree architectures, previous studies on sensor development, and efforts to develop and deploy mechanical/robotic systems for automated branch pruning. We also describe two examples of qualified pruning strategies that could potentially simplify the automated pruning decision and pruning end-effector design. Finally, the limitations of current pruning technologies and other challenges for automated branch pruning are described, and possible solutions are discussed.
The article describes the tool for designing information-active systems - the integrated environment “IS-2”: the concept of the methodology automation of intellectual labor (MAIL), the method of modeling the visual integrated environment, its structure, and features of the software solution. The Methodology Automation of Intellectual Labor (MAIL) offers an industrial way to create information-active, intelligent, automated and other systems. Their design involves the formation of models at three stages - initial modeling, conceptual modeling, and infological modeling. To automate the design process according to MAIL, a visual integrated environment (VIS) that supports this process is being developed at MSUT “STANKIN”. A formal description of the structure of the models, the structure of creating models process, the method of modeling VIS has been completed. The features of the integrated environment “IS-2”, obtained in the process of its development such as the formation of a universal (library of universal components used in the development of modules) component and specialized, are described. The composition of the versions is described including graphical editors for forming constituent models and their linking, means of intermediate documentation of models in the form of diagrams and specifications, and prototypes of modules for supporting analytical processing and synthesis of models.
U. Murray, Zewdy Gebremedhin, Galina Brychkova
et al.
Abstract Climate change and variability present a major challenge to agricultural production and rural livelihoods, including livelihoods of women smallholder farmers. There are significant efforts underway to develop, deploy, and scale up Climate-Smart Agricultural (CSA) practices and technologies to facilitate climate change adaptation for farmers. However, there is a need for gender analysis of CSA practices across different farming and cultural systems to facilitate adoption by, and livelihood improvements for, women smallholder farmers. Climate change poses challenges for maintaining and improving agricultural and labor productivity of women smallholder farmers. The labor productivity of many women smallholders is constrained by lack of access to labor-saving technologies and the most basic of farm tools. Poorer smallholders face a poverty trap, due to low agricultural and labor productivity, from which they cannot easily escape without access to key resources such as rural energy and labor-saving technologies. In Malawi, the agricultural system is predominantly rainfed and largely composed of smallholders who remain vulnerable to climate change and variability shocks. Despite the aspirations of women smallholders to engage in CSA, our research highlights that many women smallholders have either limited or no access to basic agricultural tools, transport, and rural energy. This raises the question of whether the future livelihood scenarios for such farmers will consist of barely surviving or “hanging in”; or whether such farmers can “step up” to adapt better to future climate constraints; or whether more of these farmers will “step out” of agriculture. We argue that for women smallholder farmers to become more climate change resilient, more serious attention to gender analysis is needed to address their constraints in accessing basic agricultural technologies, combined with participatory approaches to develop and adapt CSA tools and technologies to their needs in future climates and agro-ecologies.
Maleeha Naseem, Ramsha Akhund, Hajra Arshad
et al.
Background: In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems. Methods: The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR). Results: Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic : AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis : Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development : A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC) : There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare. Conclusion: AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC.
Computer applications to medicine. Medical informatics, Public aspects of medicine
Aging is associated with the onset of several diseases in various organ systems; however, different tissues may age differently, rendering some of them dysfunctional sooner than others. Placental membranes (fetal amniochorionic membranes) protect the fetus throughout pregnancy, but their longevity is limited to the duration of pregnancy. The age-associated dysfunction of these membranes is postulated to trigger parturition. Here, we investigated whether cellular senescence—the loss of cell division potential as a consequence of stress—is involved in placental membrane function at term. We show telomere reduction, p38 MAPK activation, increase in p21 expression, loss of lamin B1 loss, increase in SA-β-galactosidase, and senescence-associated secretory phenotype (SASP) gene expression in placental membranes after labor and delivery (term labor [TL]) compared to membranes prior to labor at term (term, not-in-labor [TNIL]). Exposing TNIL placental membranes to cigarette smoke extract, an oxidative stress inducer, also induced markers of cellular senescence similar to those in TL placental membranes. Bioinformatics analysis of differentially expressed SASP genes revealed HMGB1 signaling among the top pathways involved in labor. Further, we show that recombinant HMGB1 upregulates the expression of genes associated with parturition in myometrial cells. These data suggest that the natural physiologic aging of placental tissues is associated with cellular senescence and human parturition.