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The study aims to provide a critical review of the extent to which digital technologies are likely to replace human labour, the exponential rise in the amount of work to be done and how far distinctively human skills are future-proofed and therefore likely to be in short supply. It reviews the evidence for a permanent switch to home and remote working enabled by emerging technologies. It assesses the business, digital and labour strategies of work organisations and the promise and challenges from a dominant trend towards a digitally enabled flexible labour model.

A critical review of 1020 plus case studies and the extant literature was carried out.

The relationship between emerging technologies and work is widely misunderstood, and there are major qualifiers to the idea of an overwhelming tsunami of technology drastically reducing headcounts globally. Distinctive human skills remain valuable, the amount of work to be done is increasing exponentially and automation is becoming more a coping than a labour replacement mechanism. Moves to a hybrid digitalised flexible labour model are promising but not if short-term, and if the challenges they represent are not managed well.

The main limitation is that we are making projections into the future, though we are drawing on a lot of different sources and evidence and past data projected into the future.

The problem is not labour displacement but large skills shortages that will slow down the speed of technology adoption. Skills development is vital, as is the taking of long-term perspectives towards the management of hybrid, flexible working based on human-machine interactions.

Organisations need to revitalise their training and development and labour management models. Governments and intermediary institutions need to manage transition states if the skills required to gain economic growth are to be available, and to ensure that large labour pools do not get bypassed from not having requisite skills.

The study offers a more subtle and complex perspective on the emerging evidence about the future of technology and work.

Even with the Saudi Arabian Government's discretionary measures to mitigate the spread of the coronavirus disease 2019 (COVID-19), the economic sectors were not spared from the damage. Thus, the paper aims to use a computable general equilibrium (CGE) model to evaluate the impact of COVID-19 on the Kingdom of Saudi Arabia's (KSA) economy, with a special focus on small and medium enterprises (SMEs) and production. These influence the level of poverty.

The paper adopted the social accounting matrix (SAM) for Saudi Arabia built in 2021 by Imtithal Althumairi from Saudi Arabia's 2017 SAM. The model represents a snapshot of the economy and different flows that exist within the tasks and institutions. Two simulations (mild and severe) were conducted because of the focus on the distributional outcomes.

Decrease in job creation and economic growth were significant evidence from the study's findings. Findings show that more families hit below the poverty line because the negative impacts of the pandemic have shifted the income allocation curve. Findings show that the weakest of the poor are mitigated by government social grants during the pandemic.

The paper is restricted to the relevant literature relating to the impact of COVID-19 on Saudi Arabia's economy and evaluated using the SAM model. Moreover, the COVID-19 is still an ongoing scenario; thus, the model should be updated as data utilised for the operationalisation are made available.

The information from the suggested model can be suitable to measure the degree of the harm, and thus, the likely extent of the desirable policy feedback. Also, the model can be updated, as data are made available and formulated policies based on the updated data implemented by the policymakers.

Apart from the recovery planning of SMEs during the pandemic, the paper intends to stir up Saudi Arabia's policymakers through the macro-micro model to recovery planning and resilience of the economy with emphasis on mitigating unemployment.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

This study aims to analyse the differences between professional interaction mediated by video conferencing and direct professional interaction. The research identifies diverging interests of office workers for the purpose of addressing work ethical and business ethical issues of professional collaboration, competition, and power in future hybrid work models.

Based on 28 qualitative interviews conducted between November 2020 and June 2021, and through the theoretical lens of phenomenology, the study develops explanatory hypotheses conceptualising four basic intentions of professional interaction and their corresponding preferences for video conferences and working on site.

The four intentions developed on the basis of the interviews are: the need for physical proximity; the challenge of collective creativity; the will to influence; and control of communication. This conceptual framework qualifies a moral ambivalence of professional interaction. The authors identify a connectivity paradox of professional interaction where the personal dimension remains unarticulated for the purpose of maintaining professionality. This tacit human connectivity is intertwined with latent power relations. This plasticity of both connectivity and power in direct interaction can be diminished by transferring the interaction to video conferencing.

The application of phenomenology to a collection of qualitative interviews has enabled the identification of underlying intention structures and the system in which they affect each other. This research identifies conflicts of interests between workers relative to their different self-perceived abilities to persevere in competitive professional interaction. It is therefore able to address consequences of future hybrid work models at an existential and societal level.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.

This study aims to evaluate a human rights-informed dementia prevention program promoting better health and social care among older adults. In this study, the authors examined whether a dual-task training would improve cognition in healthy older adults.

Individuals attending the systematic health education program for older adults based in Japan were recruited for study inclusion, and divided into a dual-task training group (TG) and a control group (CG). The TG underwent 90 min of a weekly dual-task training for 12 weeks. Severity of dementia was measured using the Mini-Mental State Examination (MMSE) test. Brain function was assessed using a go/no-go task paradigm, during which cerebral blood flow was additionally measured using functional near-infrared spectroscopy to quantify oxyhemoglobin (oxy-Hb).

MMSE total score, number of errors in the go/no-go tasks and oxy-Hb values showed significant improvements in the TG.

Owing to the small number of participants allocated to the CG, the results must be interpreted with caution. Replication and further validation based on large-scale, randomized-controlled trials is warranted.

This study highlights potential benefits of incorporating an early prevention training for dementia into a human rights-friendly health education program.

This study suggests a potential means to reduce costs of social security and health care by introducing a human rights-informed dementia prevention program.

The results suggest that dual-task training may improve cognitive function in healthy older adults, thereby contributing to better health and improvement of social health care, based on a human rights-informed health education program for the prevention of dementia.

This paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.

The proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.

The proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.

The new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.

The goal of this study is to test the real-world use of an emotion recognition system.

The researchers chose an existing algorithm that displayed high accuracy and speed. Four emotions: happy, sadness, anger and surprise, are used from six of the universal emotions, associated by their own mood markers. The mood-matrix interface is then coded as a web application. Four guidance counselors and 10 students participated in the testing of the mood-matrix. Guidance counselors answered the technology acceptance model (TAM) to assess its usefulness, and the students answered the general comfort questionnaire (GCQ) to assess their comfort levels.

Results from TAM found that the mood-matrix has significant use for the guidance counselors and the GCQ finds that the students were comfortable during testing.

No study yet has tested an emotion recognition system applied to counseling or any mental health or psychological transactions.

The paper analyzes and develops Chiva's (2014) proposal on the common welfare HRM system and uncovers its relationship with innovativeness, using altruism as a mediator.

The common welfare HRM system implies a certain human and organizational development of the classic control and commitment HRM systems, and its main goal is to promote innovation through a prosocial approach. To this end, the authors investigated its HRM practices, developed a measurement instrument and provided initial illustrative evidence of some of its main implications for innovativeness and altruism. They tested these relationships on a sample of 269 Spanish firms using structural equations and bootstrapping to confirm the significance of the mediated effect.

Results confirm the study’s hypotheses, thus supporting the common welfare HRM system as a relevant tool for developing innovativeness through the power of altruism. This paper therefore provides empirical evidence of these relationships.

This study has implications that can help managers to increase innovativeness through a specific HRM system. The findings reveal that a coherent set of HRM practices based on common welfare principles and a high level of consciousness creates a climate of altruism that results in innovativeness.

This research shows that humanistic HRM practices also have an impact on performance variables such as innovativeness, through altruistic employees' behaviors. It also develops a measurement instrument for the common welfare HRM system and provides some initial illustrative evidence of some of its main implications.

本文分析並發展Chiva (2014) 關於共同福祉人力資源管理系統的建議;同時，擬以利他主義為中介變量、揭示共同福祉人力資源管理系統與創新意念之間的關係。

共同福祉人力資源管理系統暗示了典型的管制和承諾型人力資源管理系統的人類與組織發展;而共同福祉人力資源管理系統的主要目的是以親社會理念去鼓勵創新。為此，我們探討了共同福祉人力資源管理措施，制訂了測量儀器，並為共同福祉人力資源管理可幫助帶來創新意念和利他主義的啟示，提供了初步的例證。研究的樣本為269間西班牙公司;我們以結構方程去檢測共同福祉人力資源管理與創新意念和利他主義之間的關係，並創造環境，俾能確認有關之中介效應的重要性。

研究結果證實了我們的假設，就是說，研究結果確認了透過利他主義的影響力，共同福祉人力資源管理系統是可以成為促進創新意念的工具的。就此而言，本文提供了關於這些關係的經驗證據。

本研究顯示了人文主義的人力資源管理措施，亦可透過僱員無私的行為，影響著各個績效變量，像是創新意念。研究亦為共同福祉人力資源管理系統制訂了測量儀器，並提供了初步的例證，確認了該人力資源管理系統所給予我們的主要啟示。

本研究為管理人員提供了啟示，協助他們透過特定的人力資源管理系統去增強創新意念。研究結果顯示、建基於共同福祉的原則和高度意識水平的一整套連貫的人力資源管理措施，能創造一個引來創新意念的利他主義氣氛。