Articles

<b>Background: </b>The construction industry is one of the most hazardous sectors worldwide, with workers consistently exposed to risks from prolonged contact with hazardous materials. This study evaluates the effectiveness of wearable technologies in improving safety and health monitoring among construction workers in Nigeria. <b>Methodology</b>: A quantitative cross-sectional survey was conducted among 370 construction workers, site managers, and safety officers across Nigeria using an online questionnaire hosted on Google Forms. The study employed purposive sampling to ensure representation across job roles, experience levels, and project types. Data were collected on awareness, usage, benefits, challenges, and improvements associated with wearable technology. Data was analysed using SPSS version 28. <b>Results</b>: Findings showed that awareness of wearable technology was low (32.4%), and only 34.1% reported personal utilisation. Smart vests (32.7%), noise monitoring devices (32.4%), and smartwatches (32.4%) were the most common devices used. Benefits cited included reduced accident occurrences (28.4%) and enhanced well-being (24.6%), while barriers included cultural/language issues (32.7%) and technological limitations (22.4%). Overall, 48.6% reported improvement in safety and health monitoring. Logistic regression showed that wearable technology use significantly improved safety and health monitoring (AOR = 5.937, p < 0.001), with commercial project workers reporting higher odds of improvement (AOR = 7.495, p = 0.001). <b>Conclusion</b>: Wearable technology significantly enhances safety and health monitoring in Nigeria’s construction industry, particularly for experienced workers and commercial projects. Targeted interventions addressing awareness, training, and accessibility are crucial to overcoming barriers and maximizing its impact.

The construction industry remains one of the most hazardous sectors globally, with Nigeria experiencing a high incidence of workplace accidents despite the adoption of Occupational Health and Safety Management (OHSM) frameworks. This study evaluated the effectiveness of OHSM practices in improving workplace safety across construction companies in Nigeria’s coastal cities. A cross-sectional design was employed, combining quantitative surveys of construction workers (n = 1,400) with qualitative interviews of 35 managers and supervisors. Quantitative data were analyzed using SPSS version 28, while thematic analysis was applied to qualitative responses. Findings revealed a generally positive perception of OHSM, with 54.4% of workers rating OHS policy effectiveness as “Good” and 52.0% rating health outcomes as “Good.” However, accident frequency remained a concern, with 46.4% reporting accidents occurred “Occasionally” and 31.9% acknowledging them as “Frequent” or “Very Frequent.” Comparative analysis showed indigenous firms were rated higher in policy effectiveness and health outcomes but also reported slightly higher accident frequencies than international firms. Thematic analysis identified five key monitoring and evaluation strategies including routine inspections, regular training, audits, behavioural reinforcement, and access control, Also, five measures of OHSM effectiveness, including compliance observation, incident tracking, KPIs, employee feedback, and benchmarking. OHSM was found to positively influence project outcomes by reducing compensation costs, enhancing reputation, and improving supervision and quality of work. OHSM practices in Nigeria’s construction sector are perceived as effective in policy and health outcomes, yet accident rates remain a critical challenge. The study underscores the importance of continuous training, stricter enforcement, behavioural reinforcement, and systematic performance evaluation.

<b>Background:</b> Building Information Modelling (BIM) improves safety planning in construction by enabling visualization and simulation to identify and reduce risks. However, its adoption in Nigeria is limited. This study examines the application of BIM in enhancing safety and environmental performance on construction sites in Nigeria. <b>Methodology:</b> A quantitative cross-sectional survey was conducted using a structured online questionnaire distributed to professionals in Nigeria’s construction industry. A purposive sampling method was employed to target respondents with relevant BIM experience. Data were analysed using SPSS version 28, applying descriptive statistics, chi-square tests, and logistic regression at a 5% significance level. <b>Result:</b> Findings show that BIM was fully adopted by 7.0% of organizations, with only 19.8% of respondents using it to identify safety hazards during planning. While 76.8% reported no notable safety benefit, 19.5% identified improved risk management as the key benefit. Most respondents (80.2%) reported no noticeable environmental benefits. Among those who did, improved energy efficiency was the most cited benefit (16.4%). Respondents with 10 or more years of experience were significantly more likely to report enhanced safety and environmental outcomes (AOR = 4.555; p = 0.003) and adequate BIM utilization (AOR = 3.255; p = 0.023). Those with intermediate BIM experience were also more likely to report high enhancement (AOR = 2.857; p = 0.039) and effective tool use (AOR = 2.881; p = 0.050). <b>Conclusion:</b> This study revealed that BIM has the potential to improve construction outcomes in Nigeria if supported by training, experience, and structured implementation.

Knee osteoarthritis (KOA) is a chronic degenerative joint disorder that significantly impairs mobility and quality of life. While surgical interventions such as total knee arthroplasty (TKA) are effective in severe cases, conservative treatments are critical for early and intermediate disease management. This review evaluates the efficacy, safety, and clinical applications of both conservative and surgical treatment approaches, including lifestyle modifications, physical therapy, pharmacological interventions, regenerative medicine, and surgical procedures. The integration of a multidisciplinary approach is emphasized as a key strategy for optimizing clinical outcomes and tailoring interventions to disease severity.

<b>Background:</b> Scapulohumeral periarthritis commonly afflicts individuals in their middle age. Its etiology is multifaceted, and treatment presents a challenge with a high risk of recurrence. The main symptoms include shoulder pain and limited joint mobility, seriously affect the quality of life of the patients. Recent research indicate that acupuncture combined with bloodletting cupping can significantly improve the function of activity of shoulder joint and the pain in individuals with scapulohumeral periarthritis. However, these studies have typically been limited in scope, therefore additional research to substantiate the efficacy and safety of these intervention. <b>Methods:</b> To evaluate the efficacy of acupuncture combined with bloodletting cupping for treating patients with scapulohumeral periarthritis. We conducted an online search of databases in both Chinese and English, including PubMed, the Cochrane Library, Embase, Web of Science, CNKI, Wangfang Data, China Science and Technology Journal Database (VIP) and Chinese BioMedical Literature Database (CBM), to collect randomized controlled trials (RCTs) concerning the use of acupuncture combined with bloodletting cupping in scapulohumeral periarthritis patients. We also examined the references within the identified literature. Search utilised subject headings and free-text terms in both languages, without racial restrictions, for records up to April 3, 2024. Two researchers independently screened the literature, extracted data, and evaluated their qualities. RevMan 5.3 software was used for meta-analysis of the included studies. The protocol of this review was recorded in the International Platform of Registered Systematic Review and Meta-analysis Protocols (PROSPERO). Its registration number is CRD42023454614. <b>Results:</b> This review incorporated 22 RCTs involving a total of 1,774 patients. The results of meta-analysis showed that the clinical effective rate (RR=1.25, 95%CI [1.20, 1.30], P＜0.00001) of treating scapulohumeral periarthritis with acupuncture combined with bloodletting cupping was higher in the experimental group than in the control group. The all of Visual Analogue Scale (VAS) score (MD=-1.70, 95% CI [-2.17, -1.22], P＜0.00001). Melle score (SMD=-2.45, 95% CI [-2.55, -2.34], P=0.007]) and recurrence rate (RR=0.23, 95% CI [0.07, 0.77], P=0.02) were lower in the experimental group than in the control group with statistical significance (P<0.05). <b>Conclusion:</b> The acupuncture combined with bloodletting cupping for the treatment of shoulder impingement syndrome demonstrates definite efficacy and safety, with superior clinical effectiveness, pain relief, improvement in shoulder joint mobility, and reduction in recurrence compared to acupuncture alone. Therefore, it is worthy of being promoted and applied clinically.

<b>Objective: </b>This study explores the relationship between neighborhood stress and Body Mass Index (BMI) in adolescents, while also examining whether this association differs between Black and White adolescents. <b>Methods:</b> Data from the Adolescent Brain Cognitive Development (ABCD) Study were analyzed using linear regression models to examine the association between neighborhood stress (defined as a composite score based on three items measuring perceived safety and neighborhood fear) and BMI in adolescents, controlling for demographic and socioeconomic variables. We tested models both with and without interaction terms to assess whether race moderated the association. Stratified analyses were conducted to further explore potential differences between Black and White adolescents. <b>Results:</b> A positive association was observed between neighborhood stress and BMI across the overall sample. However, this association was weaker for Black adolescents compared to White adolescents, even after adjusting for potential confounders. <b>Conclusions:</b> The contribution of neighborhood stress to higher BMI in adolescents may vary by race. Our findings suggest that while neighborhood stress is associated with increased BMI, Black adolescents appear to be less affected by these stressors than their White peers. This weaker association could be due to the normalization of stress in environments where it is pervasive (what is common becomes normal) or the presence of other significant risk factors affecting BMI in Black youth, such as poverty, limited food access, food culture, and food deserts. Future research should explore processes of habituation, inoculation, or even sensitization to stress among Black populations, who are often exposed to a wide range of stressors throughout the life course.

Tanga City is the region with several micro-and small-scale pasta processing companies in the country. Therefore, the purpose of this study was to assess the quality and safety of folded vermicelli produced by the small-scale processors in Tanzania. Samples of 1 kg folded vermicelli were collected from 14 processing companies, by the intentional cluster sampling technique. The samples were analysed for aflatoxin and microbiological (<i>Escherichia coli</i>, <i>Aspergillus flavus</i>, and <i>Aspergillus parasiticus</i>) quality. Moreover, physico-chemical quality was assessed in terms of diameter by using a digital calliper, moisture content by oven-drying method at 110℃± 5℃, breaking strength by the texture analyzer, and colour by colourimeter (Chroma Meter CR-400) of the collected samples were determined. In terms of microbial quality, the results indicated contamination by <i>E. coli</i> (1.25-3.00 Log CFU.g<SUP>-1</SUP> in 8/14 samples), <i>A. flavus</i> (2.23-2.83 Log CFU.g<SUP>-1</SUP> in 12/14 samples), and <i>A. parasiticus</i> (1.22-2.75 Log CFU.g<SUP>-1</SUP> in 2/14 samples) as they are beyond the set limits. The diameter varied between 0.90 mm to 1.73 mm in 9/14 samples and moisture content were 10.61% to 12.65% in 13/14 samples, being within the established parameters. The samples indicated low breaking strength with levels between 6.79x10<SUP>5</SUP> N.m<SUP>-2</SUP> to 3.75x10<SUP>6</SUP> N.m<SUP>-2</SUP> in 11/14 samples. The result of brightness (L*) were between 53.03 to 72.14 and yellowness (b*) between 13.68 to 19.48 indices, indicating that there was no significant difference at the 5% level, respectively, although 2/14 samples had red (a*) colour values (-1.32 – +0.56). However, 4/14 samples were detected with aflatoxin B<SUB>1</SUB> (0.60-0.70 μg.kg<SUP>-1</SUP>), they are within the recommended level (5 μg.kg<SUP>-1</SUP>). The study underscores the need for concerted efforts to enhance production and hygiene practices to ensure consistent compliance with quality and safety standards.

This paper leverages gene and cell therapy research in diverse disorders ranging from monogenic to infectious diseases to cancer and emerging breakthroughs, where one can harness individual genes or a synthetic gene sequence designed based on a shared molecular pattern in infected cells to better fight various disorders [1]. A pivotal task is to predict the performances of candidate gene therapies to guide clinical translational research using methods such as retrospective bioinformatic analyses. Implementing them to a large-scale gene therapy database reveals that it is feasible to construct and apply well-performing interpretable, supervised learning models [2]. Preliminary evidence of machine learning approaches' statistical significance helps clinicians and biomedical researchers, market participants, and regulatory and economic experts derive relevant, practical applications, thereby enhancing the deployment of gene therapy and genomics to achieve positive, long-term growth for humanity while alleviating the ongoing worldwide economic burden precipitated by prolonged and recurring diseases. Deploying machine learning techniques to accelerate gene and cell therapy drug development and trials shall also mitigate the existing obstacle of limited patient access to emerging, transformative medical innovations such as gene therapy due to skyrocketing prices, which often herald gene therapy products as the world's most expensive medicines [3]. Moreover, in preventing patients from accessing effective, life-saving genetic medicines, there commonly exists a multidimensional access gap encompassing the availability, affordability, and quality or acceptability of these clinical treatments. The ensuing substantial gap has repeatedly been documented and mainly emanates from differential institutional and socio-political choices around resource allocation at international and domestic levels [4]. Particularly, it is also due to the stringent licensure and regulatory approval processes underpinned by insufficient evidence for novel safety and clinical efficacy profiles for genetic therapies in multiple micro-local diagnoses and subpopulations. We believe that a higher likelihood of gene therapy adoption shall result when the clinical evidence path contains adequate representation from the most diverse and relevant patient populations [5].

Failure prediction can be achieved through prognostics, which provides timely warnings before failure. Failure prediction is crucial in an effective prognostic system, allowing preventive maintenance actions to avoid downtime. The prognostics problem involves estimating the remaining useful life (RUL) of a system or component at any given time. The RUL is defined as the time from the current time to the time of failure. The goal is to make accurate predictions close to the failure time to provide early warnings. J S Grewal and J. Grewal provide a comprehensive definition of RUL in their paper "The Kalman Filter approach to RUL estimation." A process is a quadruple (XU f P), where X is the state space, U is the control space, P is the set of possible paths, and f represents the transition between states. The process involves applying control values to change the system's state over time.

<b>Introduction:</b> Cerebral venous thrombosis is a rare type of stroke caused by partial or complete occlusion of cerebral venous sinuses. Current guidelines recommend the administration of Low Molecular Weight Heparin (LMWH) during the acute phase and oral Vitamin K antagonists (VKAs) such as warfarin for 3-12 months. Direct Oral Anticoagulants (DOACs) are an attractive alternative to VKAs as therapy for CVT, for its safety and efficacy as anticoagulation therapy for deep venous thrombosis or pulmonary embolism. <b>Method:</b> This systematic review is written based on PRISMA guidelines with electronic search performed on various databases for journals published from June 1, 2018 to June 1, 2023. <b>Results:</b> We found four studies fulfilling the inclusion criteria, with four randomized controlled studies presenting 179 CVT patients treated with DOAC and 150 patients treated with standard therapy. DOACs used in reviewed studies are Dabigatran and Rivaroxaban. <b>Discussion:</b> Administration of DOACs as anticoagulation therapy in patients with CVT presents better recanalization rate with no significant differences in efficacy compared with VKAs, along with a better safety profile through similar mortality rate across two groups. <b>Conclusion:</b> DOACs as long-term anticoagulation therapy in patients with CVT has better efficacy along with a similar safety profile compared to VKA.

The supervisory role of kindergarten teachers is primarily concerned with supervising and managing the efforts of their learning environment to create safe, positive learning environments for all learners. The purpose of this study was to assess teachers’ supervisory practices of learners in Kindergarten schools in Komenda Edina Eguafo Abirem municipality. (K.E.E.A) Municipality in the Central Region of Ghana. The study employed the qualitative research approach using the interpretivism paradigm. The instrumental case study design was adopted for this study. Population of the study comprised public kindergarten schools in KEEA Municipality. A multi-stage purposeful random sampling technique was used to select sixteen (16) respondents for the study. The semi-structured interview and observation checklist were the main instruments employed in the data collection for this study. The qualitative data was analysed by the use of the interpretative method based on the themes arrived at during the data collection. The themes were related to the research question and interpreted on the number of issues raised by respondents. These were based on question on the semi-structured interview. The study indicated that, kindergarten teachers do not supervise their learners during out-door activities or at playground, thinking it is the available time to relax after learners are out to play. The study also revealed that, kindergarten teachers do not pay close attention to learners and their physical environment to detect danger and threats. Also, in relation to proximity in supervision, the study found that, kindergarten teachers do not maintain physical closeness supervision of their learners to promptly salvage them from occurring danger during supervision. It is recommended that, the Ministry of Education (MoE), Ghana Education Service (GES), and other Agencies in Education (AiE) should strategically and periodically organize workshop trainings and seminars on teacher supervision and safety for kindergarten teachers. It is also recommended that, Tertiary institutions like the teaching universities and colleges of education should mount courses specifically for Teachers Supervision and School Safety for pre-service teachers and school administrators.

Avian influenza (bird flu) occurs sporadically in American poultry flocks, decimating these flocks and causing substantial economic losses. Avian influenza also impacts the beliefs of food handlers and preparers in the home (home cooks). Although those who properly handle and prepare processed poultry products cannot succumb to avian influenza, there is a widespread belief that one can contract the bird flu from these foods. Beliefs about getting avian influenza from poultry products and intentions to avoid consuming poultry products are the focus of this study of 285 home cooks. False beliefs about getting avian influenza from handling, preparing, and consuming poultry products are apparent in this sample. Correlational analysis also shows that those holding the false beliefs intend to act upon those beliefs by planning not to consume poultry products. Moreover, the false beliefs about contracting avian influenza from poultry products are correlated with a bias to see oneself as less likely to produce food that contains foodborne diseases. These findings are consistent with, and contribute to, the research literatures on belief formation and change, behavioral intentions, and with research showing how guilt by association thought processes underlie false beliefs related to food safety. This research has important implications for poultry and other food processing industries, and for campaigns to persuade the public about real and imaginary risks associated with particular food products.

This essay explores how AI can enhance clinical research and, particularly, its pivotal role in the development of medical devices. A data-driven approach to medical device development that can streamline regulatory compliance and quality assurance is discussed. Methods that generate insights from pre-stage data and utilize it during development are detailed. The effectiveness of this approach in compliance audits, 510(k) submissions, and quality system audits - reducing time, effort, and risks is analyzed. The findings are illustrated with practical examples and takeaway recommendations. When reading a scientific article, how many times have you judged the quality of the research by looking at the methodology section? Artificial intelligence algorithms can be developed with the most robust and innovative technology, but if they are not properly validated, they will be worthless in the eyes of regulatory authorities. Conversely, outdated and simplistic models can still gain regulatory clearance if robustness is effectively demonstrated. For better or worse, ethics, economics, and robustness are often sacrificed in the constant government struggle to keep up with the technological edge of AI development. The slow crawl of lawmakers is constant in every field. Automating small tasks can save time and reduce risks when playing catch-up with a changing regulatory framework so the rest of the AI development can continue uninhibitedly. This dives into using FDA open data to collaborate with a food and drug law company and develop several bottom-up initiatives that supply knowledge needed for regulatory compliance and quality systems development. Methods that input pre-stage data and output actionable insights as models are provided. By sharing these resources and advice as academic researchers, efficiency in streamlining processes is maximized, thereby letting more time and resources be allocated to the actual development [1].

Advances in web-centric cloud computing have facilitated the establishment of an integrated cloud environment connecting a wide variety of clinical trial stakeholders. A web-centric cloud framework is proposed for real-time monitoring and risk prediction during clinical trials. The framework focuses on identifying relevant datasets, developing a data-management interface, and implementing machine-learning algorithms for data analysis. Detailed descriptions of the data-management interface and the machine-learning processes are provided, targeting active clinical trials with therapeutic uses in cancer. Demonstrations utilize publicly available clinical-trial data from the ClinicalTrials.gov repository. The real-time monitoring and risk prediction systems were assessed by developing five supervised-classification-machine-learning models for trial-status prediction and six unsupervised models for patient-safety-profile assessment, each representing a different phase of the clinical-trial process. All supervised models yielded high accuracy and area-under-the-curve values at the testing stage, while the unsupervised models demonstrated practical applicability. The results underscore the advantages of using the trial-status algorithm, the patient-safety-profile model, and the proposed framework for performing real-time monitoring and risk prediction of clinical trials.

The purpose of this study was to assess safety conditions of learners in Kindergarten schools in Komenda Edina Eguafo Abirem (K.E.E.A) Municipality in the Central Region of Ghana. The qualitative research approach and the Instrumental Case Study Design were adopted for the study. The population of the study comprised public kindergarten teachers and schools in Komenda Edina Eguafo Abirem municipality in the Central Region of Ghana. Convenient and purposive sampling techniques were used to select sixteen (16) public kindergarten teachers and eight (8) kindergarten schools for the study. The semi-structured interview and observation checklist were the instruments utilized in the data collection for this study. The data were analysed and interpreted thematically using online qualitative software, Taguette version 1.3. The study indicated that, most kindergarten school buildings in the KEEA municipality pose threats to both learners and teachers as result of over-aged or poorly constructed buildings and isolated and bushy environment which attracts animals like lizards, wall geckos and poisonous snakes and psychological and emotional threat to both teachers and learners. It is recommended that, the Ministry of Education in collaboration with the Ghana Education Service should set up a School Supervision for Safety Department (SSSD) to control and oversee safety issues in the kindergarten schools in Ghana. The School Supervision for Safety Department should create awareness advocacy for school safety so that school safety becomes a nationwide concern. It is also recommended that, the School Supervision for Safety Department must liaise with other stakeholders to enforce frequent assessment of school buildings, infrastructure and materials for safety.

The supervisory role of kindergarten teachers is primarily concerned with supervising and managing the efforts of their learning environment to create safe, positive learning environments for all learners, as well as ensuring that no child is left alone or unsupervised by teachers or caregivers while under their supervision. The purpose of this study was to examine the effects of teachers’ supervision on the safety of kindergarten pupils in Komenda Edina Eguafo Abirem (K.E.E.A.) Municipality in the central region of Ghana. Qualitatively, the Instrumental Case Study Design was employed in this study to gather information on the participants. The population consisted of 227 Kindergarten teachers in the KEEA Municipality of Ghana. Convenience sampling technique was used to select sixteen (16) public kindergarten teachers for the study. The main instrument used for data collection was semi-structured interview guide. The data were analyzed thematically. The analysis of the data was done with the help of online qualitative software, Taguette version 1.3, Using the Taguette, the researchers highlighted quotes and phrases from the interviews that were significant to the study. The study supported that, establishing a well-conducive school environment enhance teachers’ supervision which goes a long way to ensures learners’ comfortability and safety; maximize learners’ academic performance; lessen fear in learners; promote teaching and learning; and support learners’ participation in play experiences. It is recommended that, key players in education such as Ministry of Education and Ghana Education Service should investigate the effect of teacher supervision on learners’ safety vis-a-vis with its educational implications. It is also recommended that, kindergarten teachers should be encouraged to supervise their learners to guarantee positive outcomes of promoting learners’ comfortability and safety; maximizing learners’ academic performance; promoting teaching and learning; and contributing to support learners’ participation in play experiences.

The growth in demand for rail transportation systems within cities, together with high-speed and long-distance transportation running on a rail network, raises the issues of both rail safety and security. If an accident or an attack occurs, its consequences can be extremely severe. To mitigate the impact of these events, the real-time monitoring of a rail system is required. In that case, the improvements in monitoring can be achieved using artificial intelligence algorithms such as neural networks. Neural networks have been used to achieve real-time incident identification in monitoring the track quality in terms of classifying the graphical outputs of an ultrasonic system working with the rails and track bed, to predict incidents on the rail infrastructure due to transmission channels becoming blocked, and also to attempt scheduling preemptive and preventative maintenance. In terms of forecasting incidents and accidents on board the trains, neural networks have been used to model passenger behavior and optimize responses during a train station evacuation. In tackling the incidents and accidents occurring on rail transport, we contribute with two methodologies to detect anomalies in real-time and identify the level of security risk: at the maintenance level with personnel operating along the railways, and onboard passenger trains. These methodologies were evaluated on real-world datasets and shown to be able to achieve a high accuracy in the results. The results generated from these case studies also reveal the potential for network-wide applications, which could enhance security and safety on railway networks by offering the possibility of better managing network disruptions and more rapidly identifying security issues. The speed and coverage of the information generated through the implementation of these methodologies have implications in utilizing prediction for decision support and enhancing safety and security on board the rail network.

The recent evidence on AI in automotive safety shows the potential to reduce crashes and improve efficiency. Studies used AI techniques like machine learning and predictive analytics models to develop predictive collision avoidance systems. The studies collected data from various sources, such as traffic collision data and shapefiles. They utilized deep learning neural networks and 3D visualization techniques to analyze the data. However, there needs to be more research on AI in school bus and commercial truck safety. This paper explores the importance of AI-driven predictive failure analytics in enhancing automotive safety for these vehicles. It will discuss challenges, required data, technologies involved in predictive failure analytics, and the potential benefits and implications for the future. The conclusion will summarize the findings and emphasize the significance of AI in improving driver safety. Overall, this paper contributes to the field of automotive safety and aims to attract more research in this area.

The equipment used in the construction industry is usually associated with a high impact on the environment. Although sustainable design has shown to be a main player among the initiatives focused on reducing environmental impact, it has been driven by the workers and processes, leaving the equipment endeavors in more restrictive and later stages. The equipment industry has been a constant target of environmental standards and economic pressure, but the increasing technological development allows it to respond to sustainability and safety expectations while enhancing its performance. However, there are still several limitations that lead this sector to be one of the last to reach upgrading levels in terms of development. A study identified some gaps in the equipment design that require a greater effort to effectively support the workers and companies towards sustainable construction. This chapter is based on a study aiming to understand the consolidated knowledge of technologically sustainable equipment design and to identify the challenges left for its full development. The findings support the development of innovative eco-friendly equipment, taking into consideration sustainable materials and product guidelines, as well as green economy initiatives. It also supports complex system approaches and safety by design specificities to establish a corporate knowledge of sustainable equipment and align it with the new regulations of the construction industry. The chapter introduces the context of construction equipment in terms of new challenges when faced with the need to provide construction work with a greater capacity for safety, from an environmental and energy efficiency perspective, and within the paradigm of sustainability. Then, it presents the concept of sustainable equipment considering its principles, followed by a characterization of the agents involved in its life cycle.

The automation of IT Service Management (ITSM) workflows using explicit rules and data has been established for years. Domain-specific rule engines interpret rules written in declarative rule modelling languages and generate forwarding arrows to process event streams and support decision making. Such automation is augmented by rule-driven Quality Assurance for correctness, safety, and risk management. The service desk is the onshore base of an ITSM supply chain. An end-to-end incident response service resolves incidents using only onshore resources and employs back office teams to help with unresolvable incidents. The forward factories of rule-based automation for ticket processing service are identified. Several rule-based workflows in incident and change management have been published. Further glimpses of the future across all ITSM workflows are provided based on training in an online ITSM service with automated operations. Rule engines are specialised components that direct the processing of data flows according to pre-defined rules. Decision factories complement the more common event-driven rule engines. While event processing occurs below the polling frequency of the source, rules in decision factories are triggered based on the arrival of data. These factories are applied in ITSM for risk and safety evaluation and quality assurance. Rule-enriched architectures incorporate domain-specific modelling languages to ensure correctness with respect to qualitative quality attributes. Dedicated factories provide resilience, detect slack or over-utilisation, and offer point-in-time assurance and testing.