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This empirical study explores the profound impact of management functions on the productivity of yard cargo handling equipment within container terminals.

By closely examining crucial management aspects such as planning, organizing, leading, and controlling, a comprehensive managerial behavior framework was developed through focus group studies (FGS) and focal interviews. These qualitative methods were complemented by the distribution of questionnaires to practitioners in Vietnam. To validate the concept of management functions and analyze their influence on effective management practices for equipment efficiency, a structural equation model (SEM) technique was employed using partial least-squares estimation (PLS).

The findings of this study demonstrate that planning (PL), organizing (OR), and controlling (CT) significantly contribute to the productivity of yard cargo handling equipment, while leading (LD) does not exhibit a direct positive impact.

Theoretically, this study contributes by providing clarity to the definition, purpose, and value of management functions in the field of cargo handling equipment management. Furthermore, these research findings offer valuable insights to terminal operators and managers, enabling them to optimize their management strategies and enhance productivity levels, ultimately resulting in improved operational outcomes.

The integration of industry 4.0 has become a priority for many organizations. However, not all organizations are suitable and capable of implementing industry 4.0 because it requires a dynamic and flexible implementation strategy. The implementation of industry 4.0 often involves overcoming several tensions between internal and external stakeholders. This paper aims to explore the paradoxical tensions that arise for health-care organizations when integrating industry 4.0. Moreover, it discusses how a paradox lens can support the conceptualization and proposes techniques for handling tensions during the integration of industry 4.0.

This qualitative and in-depth study draws upon 32 semi-structured interviews. The empirical case concerns how two health-care organizations handle paradoxical tensions during the integration of industry 4.0.

The exploration resulted in six recurring technology tensions: technology invention (modularized design vs. flexible design), technology collaboration (automation vs. human augmentation), technology-driven patient experience (control vs. autonomy), technology uncertainty (short-term experimentation vs. long-term planning), technology invention and diffusion through collaborative efforts among stakeholders (selective vs. intensive collaboration) and technological innovation (market maintenance vs. disruption).

A paradox theory-informed conceptual model is proposed for how to handle tensions during the integration of industry 4.0. To the best of the authors’ knowledge, this is the first paper to introduce paradox theory for quality management, including lean and Six Sigma.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

The purpose of this paper is to investigate the way technology affects the provision of prehospital emergency care, upgrading the quality of services offered and significantly reducing the risk of premature termination of the patients.

The paper presents the development of the eEKAB, a pilot emergency medical information system that simulates the main services offered by the Greek National Instant Aid Centre (EKAB). The eEKAB was developed on an agile system methodology. From a technical perspective, the features and the technology were mainly chosen to provide reliable and user-friendly interfaces that will attract many users. eEKAB is based on three important pillars for offering health care to the patients: the “On-time Incident Reporting”, the “On-time Arrival at the Incident” and “Transfer to the Health Center”. According to the literature review, the emergency medical services (EMS) systems that combine all the features are very few.

It reduces the total time of the EMS procedures and it allows for an easier management of EMS, by providing a better allocation of human resources and a better geographical distribution of ambulances. The evaluation displayed that it is a very helpful application for the ambulance drivers as it reduces the ambulance response time to arrive in the patient's location and contributes significantly to the general performance of the prehospital medical care system. Also, the survey verified the importance of implementing eEKAB on a larger scale beyond the pilot usage. It is worth mentioning that the younger ambulance drivers had a more positive view for the purpose of the application.

The paper clearly identifies implications for further research. Regarding interoperability, the mobile app cooperates with the Operational Center of EKAB, while further collaboration could be achieved with other operational ambulance handling center, mainly, of the private sector. The system can evolve to include better communications among the EKAB departments. Particularly, the ambulance crew as well as the doctors should be informed with more incident features such as the emergency signal so that they know whether to open the siren, the patient's name, etc. The authors are currently working on implementing some features to provide effective medical health services to the patient in the ambulance.

eEKAB will have very significant implications in case of its enforcement, such as the reduction of the total time of EMS procedures with a corresponding reduction of the operating costs of an accident management system and an ambulance fleet handling system while in parallel informing in time the doctors/clinics. It will provide better distribution of ambulances as well as of total human resources. It will greatly assist ambulance drivers, while reducing ambulance response time to reach the patient's location. In other words, the authors will have a better performance of the whole prehospital care system.

Providing emergency care before the hospital is of great importance for upgrading the quality of health services provided at the accident site, thus significantly reducing the risk of premature death of patients. This in itself has a significant social implication.

The paper demonstrates a solid understanding in the field of the EMS systems and the corresponding medical services offered. It proposes the development of an effective, feasible and innovative EMS information system that will improve the existing emergency health care system in Greece (EKAB). An in depth literature review and presentation of the adopted new technologies and the respective architecture take place. An evaluation and statistical validation were conducted for proving the high applicability of eEKAB in case of real-life running.

This study aims to determine the influence of bruise damage generated from the impact test on the physical, chemical and nutritional responses of tomato fruit.

The impact loading was applied from different heights. The impact energies for 20, 40 and 60 cm drop heights were 129.59, 259.18 and 388.77 mJ, respectively. The injured samples were kept for 48 hours at low (10 °C) and ambient (22 °C) storage temperatures. Weight loss, firmness, color, total soluble solids (TSS), lycopene and carotenoids were measured before the impact test (day 0) and after 48 hours of the impact and storage.

The drop height of 60 cm and storage at 22 °C showed the highest values in the bruised area. The impact from the 60 cm drop height significantly reduced weight, lightness, yellowness, hue, firmness, lycopene and carotenoids, particularly at 22 °C storage condition. Redness (a*) and color index (CI) showed a remarkable increase (p < 0.05) at 22 °C on tomatoes affected from the highest impact level (388.77 mJ) after 48 hours of storage. No pronounced significance was seen between TSS and drop heights. This study has confirmed that tomato bruising for a short-term storage period induces physiological changes at different storage temperature conditions.

The study can confirm the crucial role of inappropriate handling in increasing fresh produce loss within short-term storage. Also, this research can be considered as a guideline for transporters, handlers, processors, distributors and horticulture researchers in the fresh produce supply chain during postharvest operations.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

This paper aims to examine the influence of the interplay between servant leadership behaviors and Machiavellianism on leader effectiveness.

Drawing on trait activation theory and motivation to lead theory, the authors hypothesize that the effect of servant leadership behaviors on perceived leadership effectiveness is manifested differently in teams with high-Machiavellian vs. low-Machiavellian leaders. In teams with low-Machiavellian leaders, servant leadership behaviors are expected to be associated with a cooperative way of handling team conflicts, which enhances employees' leader effectiveness ratings. In contrast, in teams with high-Machiavellian leaders, this mediation role vanishes due to the incongruency between Machiavellian traits and the cooperative context. The authors conducted a two-wave survey-based study and tested the hypotheses with a matched supervisor-employee sample from 310 employees and their leaders in 91 teams.

The results showed that servant leadership behaviors positively impact leadership effectiveness and that this effect takes place through cooperative team conflict management (TCM) without controlling for leaders' Machiavellian trait. Further analysis shows this mediation mechanism is only strong and significant in teams led by low-Machiavellian leaders, but not high-Machiavellian leaders.

To the authors’ best knowledge, this is the first study that examines the interplay of servant leadership behaviors and Machiavellianism on perceived leader effectiveness.

本文旨在探討僕人式領導行為與馬基雅維利主義之間的相互作用會如何影響領導效能。

我們根據特質激活理論和領導動機理論、建立了一個假設，這個假設就是： 僕人式領導行為對感知的領導效能所產生並顯示出來的影響、是會視乎團隊是高/強馬基雅維利主義，還是低/弱馬基雅維利主義而有所分別的。若團隊的領導者是低/弱馬基雅維利主義的話，僕人式領導行為大概會與使用合作的方式去處理團隊衝突有相互之關聯，這會提高僱員對領導效能的評分。與此相反，若團隊的領導者是高/強馬基雅維利主義的話，這調節作用和角色將會因馬基雅維利主義的特質與合作的環境之間存在著不協調而消失。我們進行了一個兩波的、以及基於調查的研究，在這研究中，我們利用管理者和員工相應的樣本來測試各個假設;這些樣本包括91個團隊內的310名員工及其領導者。

研究結果顯示、僕人式領導行為對領導效能會產生積極的影響，而這影響是透過以合作方式管理團隊衝突而產生的，亦沒有對領導者的馬基雅維利主義特質加以管控。我們進一步的分析顯示、這調節機制只會在由低/弱馬基雅維利主義的領導者領導的團隊內顯得強烈和顯著，但若領導者是高/強馬基雅維利主義的話，情況就不一樣了。

盡我們所知，本研究為首個研究、去探討僕人式領導行為與馬基雅維利主義之間的相互作用會如何影響感知的領導效能。

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

The purpose of this study was to investigate a framework for the implementation of freedom of information (FOI) legislation in South Africa, against Article 19’s nine principles of FOI legislation.

This qualitative study used semi-structured interviews to collect data from six experts selected by means of the snowball sampling technique and content analysis. The study used a modified Delphi design consisting of two rounds of interviews.

The results showed that little effort is made by government officials to demonstrate commitment to the implementation of FOI legislation.

The passing of FOI is expected to reduce corruption, increase public participation, reduce the level of secrecy and increase transparency and openness. This is not the case as the implementation of this socioeconomic right in South Africa is faced by numerous challenges, such as a lack of political will, secrecy laws providing for the opposite of what the FOI legislation seeks to achieve, poor legislative interpretation and a lack of clear policies. The study proposes a framework aimed at addressing these challenges.

The study provides a framework for the implementation of FOI legislation. The framework was developed under the guidance of Article 19 principles of freedom of information legislation.