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This article focuses on migrant labour in Nordic agriculture, wild berry picking and food processing. The starting point is the fear of a food crisis at the beginning of the coronavirus pandemic (2020) because of the absence of migrant workers. The question was raised early in the pandemic if food systems in the Global North are vulnerable due to dependence on precarious migrant workers. In the light of this question, we assess the reactions of farmers and different actors in Denmark, Finland, Norway and Sweden to what looked like an unfolding food crisis. In many ways, the reactions in the Nordic countries were similar to each other, and to broader reactions in the Global North, and we follow these reactions as they relate to migrant workers from an initial panic to a return to business as usual despite the continuation of the pandemic. In the end, 2020 proved to be an excellent year for Nordic food production in part because migrant workers were able to come. We discuss reasons why the Nordic countries did not face disruptions during the pandemic, map out patterns of labour precarity and segmentation for migrant labour in agriculture and food production in the Nordic countries and propose questions for further research. [ FROM AUTHOR] Copyright of Sociologia Ruralis is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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The pandemic situation is affected in various ways in the education domain. The sudden transformation from offline to online teaching-learning process made students and teachers use different tools like WhatsApp for communication. The reason for this consideration is to investigate the impacts of WhatsApp utilized for instruction and decide the suppositions of understudies towards the method. The study is designed, keeping in mind the current COVID-19 situation and how it affected the education system turning it into online mode. On different questionnaires, regression and heatmap analysis is performed. The investigation showed that both learning situations have diverse impacts on the victory of understudies while supporting the conventional environment by utilizing WhatsApp is more successful for the increment of victory. The assessment moreover showed that students had superior pleasant reviews closer to the usage of WhatsApp in their courses. They requested the same workout in their one-of-a-kind courses as well. They expressed that picking up information can moreover take out unwittingly and the messages with pics were more prominent and viable for their picking up information. Be that as it may, some college understudies have communicated harming audits approximately the timing of a few posts and the repetitive posts within the bunch. At long last, it is supported that the utilization of WhatsApp within the preparing framework is to be energized as a steady innovation. . © 2023 IEEE.
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In 2019, the Novel Coronavirus Disease (COVID-19) was categorized as a pandemic. This disease can be transmitted via droplets on items or surfaces within several hours. Therefore, the researchers aimed to develop a wirelessly controlled robot arm and platform capable of picking up objects detected via object detection. Robot arm movements are done via the use of inverse kinematics. Meanwhile, a custom object detection model that can detect objects of interest will be trained and implemented in this project. To achieve this, the researchers utilize various open-source libraries, microcontrollers, and readily available materials to construct and program the entire system. At the end of this research, the prototype could reliably detect objects of interest, along with a grab-and-dispose success rate of 88%. Instruction data can be properly sent and received, and dual web cam image transfer reaches up to 1.72 frames per second. © 2023 IEEE.
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PurposeThis study examines the different effects of service recovery strategies on customers' future intentions when online shoppers were experiencing delivery failures. Two types of problem severity are evaluated: wrong-product delivery (issues with the product quality or quantity) and late delivery. This study also investigates the impact of service criticality on the relationship between service recovery strategies and customers' future intentions.Design/methodology/approachThis study employs experimental research with 123 online shoppers as participants. Following the results, a subsequent test is conducted to examine the effect of participants' demographics on future intentions. Finally, the current study elaborates the findings using qualitative research, interviewing both sides impacted by the service failures: online shoppers and e-retail managers.FindingsThe findings show that complementing product replacement with monetary compensation is the most effective strategy to improve repurchase intention after a dissatisfaction moment. This effect is indifferent to service criticality and severity. Age influences the participants' repurchase intentions, in which younger people are less tolerant of service failures. In contrast, gender and education level do not provide any differences. To prevent delivery failures, managers participating in this study suggest several best practices regarding systems and infrastructure, people and coordination and collaboration with logistics partners.Research limitations/implicationsThe study mainly examines a limited type of service and service failures. Further studies are encouraged to expand the variables and scenarios, as well as to employ more distinctive methods, to enrich the findings related to recovery strategy in the e-commerce industry.Practical implicationsGiven proper compensation, service failure could create momentum for online retailers to boost customer loyalty. This study suggests that managers design the most effective service recovery to win customers back to the business.Originality/valueThis paper enriches the literature related to a service recovery strategy, particularly within the online shopping context.
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Municipal solid waste could potentially transmit human pathogens during the collection, transport, handling, and disposal of waste. Workers and residents living in the vicinity of municipal solid waste collection or disposal sites are particularly susceptible, especially unprotected workers and waste pickers. Recent evidence suggests that municipal solid waste-mediated transmission can spread the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to humans. Such risks, however, have received little attention from public health authorities so far and may present an under-investigated transmission route for SARS-CoV-2 and other infectious agents during pandemics. In this review, we provide a retrospective analysis of the challenges, practices, and policies on municipal solid waste management during the current pandemic, and scrutinize the recent case reports on the municipal solid waste-mediated transmission of the coronavirus disease 2019 (COVID-19). We found abrupt changes in quantity and composition of municipal solid wastes during the COVID-19. We detail pathways of exposure to SARS-CoV-2 and other pathogens carried on municipal solid wastes. We disclose evidence of pathogenic transmission by municipal solid waste to humans and animals. Assessments of current policies, gaps, and voluntary actions taken on municipal solid waste handling and disposal in the current pandemic are presented. We propose risk mitigation strategies and research priorities to alleviate the risk for humans and vectors exposed to municipal solid wastes.
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PurposeDrawing on the concept of superior resource, capability and processes of the resource-based theory of the firm, the purpose of the current study is to analyze the influence of firms' winner-picking strategic approach on firm performance (FP) via a direct and indirect mechanism.Design/methodology/approachUsing survey data of 104 diversified manufacturing firms, the current study analyzed the conditional indirect effect of firms' strategic approach on efficient resource allocation with the help of Statistical Analysis Software (SAS) process macros.FindingsThe study found that firms' choices of winner-picking approach can undermine the resource allocation efficiency when not perfectly blended with firms' access to the resource. Furthermore, the effect of winner-picking strategy (WPS) on resource allocation efficiency via firms' competitive advantage (CA) can be greater when both strategic choice and resources are employed adequately.Research limitations/implicationsDespite making a unique contribution, the present study has a few limitations requiring researchers' attention to be tackled in the forthcoming. This includes a little amount of data, a self-reporting technique and failure to include all the possible reasons that could lead to inefficient resource allocation.Practical implicationsThe present research has potential applications for managers of the manufacturing industry in a period of sheer uncertainty [coronavirus disease 2019 (COVID-19)]. First, the study alerts managers about the challenges of underinvestment and overinvestment while allocating resources. At the same time, this study provides an important implication for managing the importance of firms' access to capital (AC).Originality/valueThe current study has made a sizeable impression in the literature on internal resource allocation and resource-based theory of the firm by recommending a model that augments the theoretical foundation of strategic management of the firms. As there are only a handful of studies on this grave issue in the context of developing economies, thus, closely considering these insights would be helping for the firms for allocating resources efficiently in the manufacturing industry.
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Background: Trichotillomania and skin picking are two forms of body focused repetitive behaviors [BFRBs] classified among Obsessive Compulsive Disorders. Socio-governmental changes which had accompanied COVID-19 overwhelmed patients with BFRBs whom already experienced anxiety and social isolation. Our study was designed to determine if there is an association between COVID-19 pandemic and worsening of symptoms of BFRB disorders ( in particular, trichotillomania and skin picking patients). Methods: Cross-sectional online survey-based study conducted from June to August 2021. The survey collected data about participants sociodemographic, knowledge, concerns, and psychological impacts by using Massachusetts General Hospital Hair Pulling Scale ( MGHHPS) and/or modified skin picking scale-revised (SPS-R). Results: A total of 171 participants joined the study including 34 (19.9%) male and 137 (80.1%) females. There was a significant difference of the total modified SPS-R (max 32), the mean score has increased by 7.62 during COVID-19 (t=6.42, p<0.001). Also, 7 parameters (subscales) are statistically significant. There was a significant difference of Frequency of urges, the mean score has increased during COVID-19 by 1.33 (t=3.16, p<0.05=0.025). Conclusion: Throughout COVID-19 pandemic in Saudi Arabia, results revealed clearly the significant negative psychological impact of it on the population, specifically on those with BFRBs. The study gives a clue that both diseases are under-diagnosed, hence, the authors suggest conducting community screening programs for early and proper management. We suggest providing more attention and further protective psychological strategies during such stressful situations that go parallel with the physical health care plans.
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With the rapid increase of digitization and desire for contactless shopping during the COVID-19 pandemic, online grocery sales keep growing fast. Correspondingly, optimized policies for order picking are nowadays central in omnichannel supply chains, not only within dedicated warehouses but also in grocery stores while processing online orders. In this work, we apply the Buy-Online-Pick-up-in-Store concept and optimize the in-store picking and packing procedure. The approach we propose, which is based on two mathematical programming models, guides pickers on how to organize articles into bags while collecting items. In this way bags are filled up evenly and they are ready to be handled to the customers at the end of each picking task, with no further rearrangement needed.
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Community retail is an important research issue in the field of fresh agriproduct e-commerce. This paper focuses on the problem of last-mile multi-temperature joint distribution (MTJD), which combines time coupling, order allocation, and vehicle scheduling. Firstly, according to the temperature of a refrigerated truck in multi-temperature zones, a split-order packing decision is proposed to integrate the different types of fresh agriproduct. Then, the order allocation strategy is incorporated into a comprehensive picking and distribution schedule, while taking into account the time-coupling of picking, distribution, and delivery time limit. To improve consumer satisfaction and reduce order fulfillment costs, an optimization model combining multi-item order allocation and vehicle scheduling is established, to determine the optimal order allocation scheme and distribution route. Finally, taking fresh agriproduct community retail in the Gulou District of Nanjing as an example, the effectiveness and feasibility of the model are illustrated. The numerical results of medium- to large-scale examples show that, compared with the variable neighborhood search algorithm (VNS) and genetic algorithm (GA), the mixed genetic algorithm (MGA) can save 29% of CPU time and 65% of iterations. This study considers the integrated optimization of multiple links, to provide scientific decision support for fresh agriproduct e-commerce enterprises.
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The COVID-19 pandemic has caused critical challenges for e-commerce warehouses that strive to fulfill surging customer demand while facing a high virus infection risk. Current literature on picking optimization overlooks warehouse safety under pandemic conditions. Meanwhile, scattered storage and zone-wave-batch picking have been used in parallel by many large e-commerce warehouses, these two operational policies have not been considered together in picking optimization studies. This paper fills these gaps by solving an order batching problem considering scattered storage, zone-wave-batch picking, and pickers' proximity simultaneously. We formulate and solve the mathematical model of the discussed problem and propose the Aisle-Based Constructive Batching Algorithm (ABCBA) to help warehouses pick more efficiently and safely. Experiments with extensive datasets from a major third-party logistics (3PL) company show that, compared to the current picking strategy, ABCBA can reduce the total picking time and the virus infection risk due to pickers' proximity by 46% and 72%, respectively. Compared to other heuristics like tabu + nLSA3 (Yang, Zhao, and Guo 2020), ABCBA gets better results using less computation time.
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In many cases, especially at the beginning of epidemic disaster, it is very important to be able to determine the severity of illness of a given patient. Picking up the severe status will help in directing the effort in a proper way. At the beginning, the number of classified status and the available data are limited, so, in such situation, one needs a system that can be trained based on limited data to give a trusted result. The current work focuses on the importance of the bioscience in differentiation between recovered patients and mortalities. Even with limited data, the decision trees (DT) was able to distinguish between recovered patients and mortalities with accuracy of 94%. Shallow dense network achieved accuracy of 75%. However, when a 10-fold technique was followed with the same data, the net achieved 99% of accuracy. The used data in this work was collected from King Faisal hospital in Taif city under a formal permission from the health ministry. PCA analysis confirmed that there are two parameters that have the greatest ability to differentiate between recovered patients and mortalities. ROC curve reveals that the parameters that can differentiate between recovered patients and mortalities are calcium and hemoglobin. The shallow net gives an accuracy of 92% when trained using calcium and hemoglobin only. This paper shows that with a suitable choosing of the parameters a small decision tree or shallow net can be trained quickly to decide which patient needs more attention so as to use the hospitals resources in a more reasonable way during the pandemic. All codes and data can be accessed from the following link “codes and data” © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.
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Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet selection methods are unsuitable for the big dataset. Therefore, we proposed a novel wavelet selection method considering the big dataset for seismic signal intelligent processing. The relevance r is calculated using the seismic waveform’s correlation coefficient and variance contribution rate. Then values of r are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value μ and variance value σ2 of that set, we define the decomposition stability w as μ/σ2. Then, the wavelet that maximizes w for this dataset is considered to be the optimal wavelet. We applied this method in automatic mining-induced seismic signal classification and automatic seismic P arrival picking. In classification experiments, the mean accuracy is 93.13% using the selected wavelet, 2.22% more accurate than other wavelets generated. Additionally, in the picking experiments, the mean picking error is 0.59 s using the selected wavelet, but is 0.71 s using others. Moreover, the wavelet packet decomposition level does not affect the selection of wavelets. These results indicate that our method can really enhance the intelligent processing of seismic signals.
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Objectives: Skin-picking disorder (SPD), also known as excoriation disorder, neurotic excoriation or dermatillomania, is characterized by repetitive and compulsive picking of skin leading to tissue damage. It is an obsessive-compulsive and related disorder that is classified with other body-focused repetitive-behavior disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The diagnostic criteria for SPD require recurrent picking, resulting in skin lesions that cause clinically significant distress or impairment in social, occupational or other important areas of functioning. Method:We present a case of a 12-years-old girl with one-year history of multiple pruritic papules, plaques and excoriations on the face, scalp, arms and legs. Skin changes appeared following COVID-19 infection, which patient described as extremely stressful. The patient did not admit scratching of the skin but examination showed a high level of shame and embarrassment associated with her skin appearance. Results: Laboratory findings were within normal rage. Skin biopsy was performed and histopathology results confirmed secondary skin lesions (iatrogenic excoriations). Based on clinical findings and psychological evaluation the patient was diagnosed with pathological skin picking. The skin lesions were treated with topical emollients under occlusion and the combination of topical corticosteroids and antibiotics. The patient also received Habit-reversal therapy (HRT). The evaluation on follow-up appointment showed favorable improvement in cognitive status and skin lesions and she remained under our supervision. Discussion: SPD often manifests in automatic or focused patterns. In automatic type, patients are unaware of their picking habits and have less conscious awareness than those with focused type. HRT is the core treatment and consists of awareness training (self-monitoring forms), stimulus control, competing-response training, social support and generalization of skills. A compassionate approach during the diagnostic and treatment could positively influence the treatment outcome.
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Purpose>This paper identifies, configures and analyses a solution aimed at increasing the efficiency of in-store picking for e-grocers and combining the traditional store-based option with a warehouse-based logic (creating a back area dedicated to the most required online items).Design/methodology/approach>The adopted methodology is a multi-method approach combining analytical modelling and interviews with practitioners. Interviews were performed with managers, whose collaboration allowed the development and application of an empirically-grounded model, aimed to estimate the performances of the proposed picking solution in its different configurations. Various scenarios are modelled and different policies are evaluated.Findings>The proposed solution entails time benefits compared to traditional store-based picking for three main reasons: lower travel time (due to the absence of offline customers), lower retrieval time (tied to the more efficient product allocation in the back) and lower time to manage stock-outs (since there are no missing items in the back). Considering the batching policies, order picking is always outperformed by batch and zone picking, as they allow for the reduction of the average travelled distance per order. Conversely, zone picking is more efficient than batch picking when demand volumes are high.Originality/value>From an academic perspective, this work proposes a picking solution that combines the store-based and warehouse-based logics (traditionally seen as opposite/alternative choices). From a managerial perspective, it may support the definition of the picking process for traditional grocers that are offering – or aim to offer – e-commerce services to their customers.
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The growing demand for food supplies, caused by the global population growth, and the continuously diminishing natural resources are posing many serious challenges. Furthermore, the economic and social uncertainty and the restrictions incurred by the recent COVID-19 pandemic are favouring the research for further fruit harvesting alternatives. The modernization of agriculture, involving interoperation among networking, machine learning, robotics, and big data entities seems to provide a promising direction to tackle the abovementioned problems. These systems, officially called cyber physical ones, can have a key role in the digital transformation of the agricultural sector, provided that people getting involved, from students and scientists of agricultural engineering to farmers and consulting professionals, are capable of understanding and using the diverse set of innovative applications belonging in this area. In this regard, taking advantage of the wide availability of cheap electronic components and suitable programming environments, modern educational practices can be further updated to provide tailored agricultural engineering solutions. The work presented herein describes the efforts to utilize a small robotic arm to identify and pick small fruits, assisted by a smart camera and a voice-commanded module. Despite the selection of low cost commercial off-the-self components and the small size of the final implementation, the main challenges towards the realization of a full-scale system, suitable for farm use, are fluently outlined while additional evaluation results are provided, from the educational point of view, as well. © 2021 ACM.
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Picking operations is the most time-consuming and laborious warehousing activity. Managers have been seeking smart manufacturing methods to increase picking efficiency. Because storage location planning profoundly affects the efficiency of picking operations, this study uses clustering methods to propose an optimal storage location planning-based consolidated picking methodology for driving the smart manufacturing of wireless modules. Firstly, based on the requirements of components derived by the customer orders, this research analyzes the storage space demands for these components. Next, this research uses the data of the received dates and the pick-up dates for these components to calculate the average duration of stay (DoS) values. Using the DoS values and the storage space demands, this paper executes the analysis of optimal storage location planning to decide the optimal storage location of each component. In accordance with the optimal storage location, this research can evaluate the similarity among the picking lists and then separately applies hierarchical clustering and K-means clustering to formulate the optimal consolidated picking strategy. Finally, the proposed method was verified by using the real case of company H. The result shows that the travel time and the distance for the picking operation can be diminished drastically.
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Customers shifting from stationary to online grocery shopping and the decreasing mobility of an ageing population pose major challenges for the stationary grocery retailing sector. To fulfill the increasing demand for online grocery shopping, traditional bricks-and-mortar retailers use existing store networks to offer customers click-and-collect services. The current COVID-19 pandemic is substantially accelerating the transition to such a mixed offline/online model, and companies like the one behind this study are facing the urgent need of a re-design of their business model to cope with the change. Currently, a majority of the operations to service online demand consists of in-store picker-to-parts order picking systems, where employees go around the shelves of the shop to pick up the articles of online orders. The optimization of such operations is entirely left to the experience of the staff at the moment. Since in-store operations are a major cost-driver in retail supply chains, this paper proposes optimization ideas and solutions for these in-store operations. With experimental simulations run on a real store with real online orders, we show that a simple optimization tool can improve the situation substantially. The method is easy to apply and adaptable to stores with complex topologies. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Aim: Skin-picking (excoriation) disorder is considered as a form of maladaptive coping methods used by individuals who have difficulties in applying more adaptive strategies. Skin-picking development has been suggested to be preceded by traumatic life events. Dissociative symptoms have been reported as experienced by skin-picking sufferers during picking episodes. The purpose of the study was to examine whether the link between trauma and automatic type of skin-picking is mediated by the frequency of dissociative experiences, and whether the COVID-19 pandemic conditions have changed this relationship in any way. Methods: The study sample consisted of 594 adults (76% women) aged from 18 to 60. Traumatic life events, dissociative experiences, and types of skin-picking (focused vs. automatic) were assessed with self-report questionnaires. Mediation analyses and multigroup path analyses were carried out. Results: Dissociative experiences partially mediated the link between traumatic events and both types of skin-picking. The model was robust considering the conditions in which survey was filled out (pre-pandemic vs. pandemic). Conclusions: Traumatic life events and dissociative experiences are associated with both automatic and focused skin-picking regardless of pandemic conditions. Further studies are needed to understand mechanisms underlying the relationship between dissociation and skin-picking styles.
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During the COVID-19 pandemic many individuals are exposed to stress of unknown duration, and due to prolonged stay-at-home period they are cut off from access to many effective coping strategies. This situation may exaggerate the use of maladaptive coping methods that are triggered by stress and boredom, and may be adopted in isolation, such as pathological skin picking. The aim of our study was to investigate the change in skin picking behaviours during the pandemic in comparison with the time prior to the pandemic onset. We also tested whether applying cognitive reappraisal as an coping strategy may affect skin picking. Self-report questionnaires measuring: automatic and focused skin picking, cognitive reappraisal, the experience of stress and loneliness were administered online to a non-clinical sample three times: 1) before the pandemic, 2) during mandatory stay at home; 3) at the time when most strict restrictions were lifted. Linear mixed-effects models were used to analyse the data. Cognitive reappraisal was found to be negatively associated with focused skin-picking regardless of the time of the measurement. In case of automatic skin picking, the link with cognitive reappraisal was significant only at the baseline and disappeared during the pandemic.