High-risk Tasks Research Articles

Design of AGV Chassis Motion Control System Based on MWorks

With the mature and extensive application of robotics, task-based mobile robots for special environments and high-risk tasks are also gaining more and more attention. To make the task-based mobile robots achieve more precise and difficult work, then, it is particularly important to achieve precise and stable control of the AGV. Therefore, by establishing a mathematical model of AGV and conducting a kinematic analysis, we design a control system that can make AGV achieve stable and accurate motion. The experimental simulation, separated from the traditional foreign simulation software MATLAB, is based on the domestic independently developed virtual simulation experiment environment MWorks. Sysplorer validated the control algorithm and virtual model AGV by digital twinning.

Exposure to Ergonomic Risk Factors to Veterinary Technicians at a Small Animal Clinic

Highlights Neck and shoulder discomfort found to be most prevalent in veterinary technicians. REBA posture analysis identifies areas of significant risk and provides a benchmark for improvements. Patient restraint and handling put veterinary technicians at significant risk for musculoskeletal disorders. Continuous improvement through the implementation of engineering and administrative controls is mandatory. Abstract. Research addressing work-related musculoskeletal disorders (WMSDs) among veterinary technicians is limited. Veterinary technicians are exposed to several activities that require lifting and the adoption of non-neutral postures associated with WMSDs. It is essential to design interventions aimed at reducing WMSDs in this population. The goal of this study was to identify typical, prevalent work tasks that pose high ergonomic risk factors to veterinary technicians, as well as to determine the WMSD risk associated with the work tasks identified in order to implement interventions. A small-animal veterinary clinic in central Wisconsin was contacted for this study. Nine veterinary technicians and assistants participated in assessing musculoskeletal symptoms and identifying tasks with ergonomic risk factors. The tasks with a high count of risk factors were evaluated using the Rapid Entire Body Assessment (REBA). All participants in this study experienced musculoskeletal discomfort in five or more body regions. They also reported being unable to perform work over the last 12 months due to WMSDs on shoulders, ankles, and feet (33%, n=3), neck and lower back (22%, n=2), and wrists, hands, hips, thighs, and knees 11% (n=1). One of the respondents reported no discomfort over the last seven days, while eight of them indicated discomfort in three or more body areas. Restraining and handling animals, sedation and recovery, and dental procedures were chosen for REBA analysis. Restraining and handling animals had a high-risk REBA score (ranging from 8 to 10). Sedation and recovery had a low to medium-risk REBA score (ranging from 3 to 5). Dental procedure recovery had a low to very high-risk REBA score (ranging from 2 to 11). Our findings suggest that most veterinary technicians come to work experiencing musculoskeletal discomfort and are exposed to significant WMSD risk levels when performing common tasks. This job assessment allows us to recommend administrative and engineering controls to reduce the risk of WMSDs associated with high-risk tasks. Keywords: Ergonomic risk factors, Musculoskeletal symptoms, Rapid Entire Body Assessment (REBA), Veterinary technicians, Work-related musculoskeletal disorders (WMSDs).

Design and Development of Aviation Aircraft Maintenance Training Platform Based on VR Technology

The maintenance process of aviation aircraft is a complex and high-risk task. This article aims to design an aviation aircraft maintenance training platform based on Virtual Reality (VR) technology to improve the training efficiency and quality of aviation aircraft maintenance personnel. By analyzing the maintenance process of aviation aircraft and combining VR technology, an interactive training platform is designed that can simulate real maintenance scenarios and provide a comprehensive training experience. The research results indicate that in terms of operational efficiency, the on time completion rate of maintenance implementation for aviation aircraft maintenance training platforms designed using VR technology has increased from 79% before optimization to 94%. The aviation aircraft maintenance training platform based on VR technology has high effectiveness and practicality, which can effectively improve the training efficiency and quality of aviation aircraft maintenance personnel.

Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task

Understanding the recommendations of an artificial intelligence (AI) based assistant for decision-making is especially important in high-risk tasks, such as deciding whether a mushroom is edible or poisonous. To foster user understanding and appropriate trust in such systems, we assessed the effects of explainable artificial intelligence (XAI) methods and an educational intervention on AI-assisted decision-making behavior in a 2 × 2 between subjects online experiment with N=410 participants. We developed a novel use case in which users go on a virtual mushroom hunt and are tasked with picking edible and leaving poisonous mushrooms. Users were provided with an AI-based app that showed classification results of mushroom images. To manipulate explainability, one subgroup additionally received attribution-based and example-based explanations of the AI’s predictions; for the educational intervention one subgroup received additional information on how the AI worked. We found that the group that received explanations outperformed that which did not and showed better calibrated trust levels. Contrary to our expectations, we found that the educational intervention, domain-specific (i.e., mushroom) knowledge, and AI knowledge had no effect on performance. We discuss practical implications and introduce the mushroom-picking task as a promising use case for XAI research.

Operating room team strategies to reduce flow disruptions in high-risk task episodes: resilience in robot-assisted surgery

In healthcare work settings, flow disruptions (FDs) pose a potential threat to patient safety. Resilience research suggests that adaptive behavioural strategies contribute to preventing cognitive overload through FDs at crucial moments. We aimed to explore the nature and efficacy of operating room (OR) team strategies to prevent FDs in robot-assisted surgery. Within a mixed-methods design, we first asked surgical professionals, which strategies they apply, and secondly, identified behavioural strategies through direct observations. Findings were analysed using content analysis. Additionally, FDs were assessed through live observations in the OR. The sample included four interviewed experts and 15 observed surgical cases. Sixty originally received strategies were synthesised into 17 final OR team strategies. Overall, 658 FDs were observed with external FDs being the most frequent. During high-risk episodes, FDs were significantly reduced (p < 0.0001). The identified strategies reveal how OR teams deliberatively and dynamically manage and mitigate FDs during critical tasks. Our findings contribute to a nuanced understanding of adaptive strategies to safeguard performance in robot surgery services. Practitioner Summary: Flow disruptions (FDs) in surgical work may become a severe safety threat during high-risk situations. With interviews and observations, we explored team strategies applied to prevent FDs in critical moments. We obtained a comprehensive list of behavioural strategies and found that FDs were significantly reduced during a specific high-risk surgical task. Our findings emphasise the role of providers’ and teams’ adaptive capabilities to manage workflow in high-technology care environments.

Spatio-temporal convolutional emotional attention network for spotting macro- and micro-expression intervals in long video sequences

Emotional detection based on facial micro-expressions is essential in high-risk tasks such as criminal investigation or lie detection. However, micro-expressions often occur in high-risk tasks when people often use facial expressions to conceal their actual emotional states. Therefore, spotting macro- and micro-expression intervals in long video sequences has become hot research. Considering the difference in duration and facial muscle movement intensity between macro- and micro-expression, we propose a novel Spatio-temporal Convolutional Emotional Attention Network (STCEAN) for spotting macro- and micro-expression intervals in long video sequences. The spatial features of each frame in the video sequence are extracted through the convolution neural network. Then the emotional self-attention model is used to analyze the temporal weights of spatial features in different emotional dimensions. The emotional weights in the temporal dimension are filtered for spotting macro- and micro-expressions intervals. Finally, the STCEAN model is jointly optimized by the dual emotional focal loss of macro- and micro-expression to solve the problem of sample unbalance. The experimental results on the CAS(ME)2 and SAMM-LV datasets show that the STCEAN model achieves competitive results in the Facial Micro-Expression Challenge 2021.

Standard Precautions are for everyone: The role of HIV stigma and implications for nursing education in India.

Standard Precautions (SP) are infection control procedures universally applicable to every patient. Though SP reduces disease transmission, their implementation is dependent on the knowledge and skills of healthcare workers (HCWs). Poor knowledge regarding the appropriate use of SP can cause fear among HCWs, leading to stigma and discrimination while treating people living with HIV (PLWH). Stigma and discrimination are known barriers for PLWH to access HIV care services. The aim of the study was to assess nursing student knowledge of SP, SP self-efficacy and SP perceived efficacy of nursing students, and (2) to assess the association between SP knowledge, perceived efficacy, and intention to utilize unwarranted precautions, like using double gloves while treating PLWH. This paper analyzes baseline (non-randomized) data of a cluster randomized controlled trial amongst 1868 Indian nursing students. Data was collected using computer-administered structured questionnaire. The associations between the measures were done using multiple, logistic and poisson regression models. Although 97% nursing students could identify SP, only 35.5% understood that they need to be used with all patients. Awareness of the importance of using SP with all patients was positively associated with self-efficacy. Students performing high-risk tasks frequently were significantly more likely to be confident in their ability to correctly use SP, but also had higher intention to use unwarranted precautions. Existing teaching and training programs for HCWs need to provide clear guidelines and emphasize on the correct use of SP with all patients. This will increase both skills and confidence in their abilities (self-efficacy).

Kitlesel İmalat Sistemlerinde Dijital İkiz Kullanılarak Gerçek Zamanlı Üretim Çizelgeleme ve Tekstil Sektöründe Bir Uygulama

Üretim ortamının dinamik yapısı planlanmayı güçleştirmektedir. Gelişen teknoloji ile birlikte dijital ikiz, üretim süreçlerine etkin çözümler sunabilmek amacıyla artan bir şekilde kullanılmaya başlanmıştır. Bu çalışmada, kitlesel imalat sistemlerinde üretimin planlanması amacıyla simülasyon temelli dijital ikiz ile bir çizelgeleme modeli önerilmiştir. Böylece çözümü zor olan üretim çizelgeleme problemlerine yenilikçi ve uygulanabilir bir yaklaşım önerilmesi amaçlanmıştır. Üretim hattının bir kopyası olan ve gerçek zamanlı veri ile entegre edilmiş model, hammadde stokları, üretim süreleri, hazırlık süreleri, vardiya değişimleri ve mola zamanları, bakım ve arıza kaynaklı duruşlar gibi birçok üretim parametresini dikkate almaktadır. Önerilen yaklaşımın gerçek hayat uygulamasını göstermek amacıyla, kumaş imalatı yapan bir tekstil işletmesinde üretim hattının dijital ikizi oluşturulmuş ve örgü makineleri için işlerin sıraları belirlenmiştir. Model ile her bir siparişin zamanında tamamlanma olasılığı belirlenerek risk analizi yapılabilmektedir. Böylece yüksek riske sahip siparişler yeniden çizelgelenerek olası gecikmelerden kaçınılabilmektedir.

Profiling the absolute and relative strength of a special operations police unit

BackgroundSpecialist police perform high-risk tasks and are required to have, and maintain, a high level of fitness. The aims of this study were to profile the strength of a specialist police unit and to investigate whether this profile remained constant over an 18-month period.MethodsRetrospective data for 47 special operations police officers (mean initial weight = 88.84 ± 8.25 kg) were provided. Officers were tested five times over 18 months for 1 repetition maximum: bench press, squat, deadlift, and pull-up. All officers continued to participate in their typical physical conditioning programs. Repeated-measures ANOVAs with Bonferroni post-hoc adjustments or Friedman tests with Wilcoxon signed-rank tests were used to compare strength values across all five time points (TPs). Alpha levels were set at 0.05.ResultsAll strength values increased significantly over the 18-month period. Over the five TPs, absolute squat increased the most (+ 9%: initial mean = 125.79 ± 24.53 kg), followed by absolute bench press (+ 8%: initial mean = 109.67 ± 19.80 kg), absolute deadlift (+ 7%: initial mean = 151.64 ± 26.31 kg) and absolute pull-up (+ 4%: initial mean = 121.43 ± 14.91 kg). Relatively, the highest increase was found with the squat (+ 8%: initial mean = 1.42 ± 0.25%), followed by the bench press (+ 7%: initial mean = 1.24 ± 0.20%), deadlift (+ 6%: initial mean = 1.71 ± 0.25%) then pull-up (+ 4%: initial mean = 1.37 ± 0.15%). The period between TP3 and TP4 yielded the fewest significant increases compared with other TP differences with only absolute bench press (+ 1.7%), absolute squat (+ 1.1%) and relative bench press (+ 1.6%) changing significantly (p < 0.05).ConclusionsSpecialist police can maintain, even increase strength, while serving in specialist units if provided with a Strength and Conditioning coach and time to train. Given changes over time, constant monitoring is required and a single timepoint may not be optimal to establish normative data.

A Method for Identifying the Key Performance Shaping Factors to Prevent Human Errors during Oil Tanker Offloading Work

Oil tanker offloading is a human-related and high-risk task. A small operational error may trigger catastrophic accidents such as fire and explosion. It is recognised that more than 70% of industrial accidents are blamed for human errors, so preventing them is crucial. As human error is associated with a variety of Performance Shaping Factors (PSFs), it is meaningful to identify key PSFs for safe operations during oil tanker offloading process. However, some issues are obstacles to finding the crucial PSFs. The recording data of most PSFs are always incomplete and imperfect. Moreover, the standard for ranking PSFs should be rational. In addition, the performance of each PSF at the different stages is oil offloading is usually unstable and may change with time. As a result, this study aims to conduct a method that mainly relies on Grey Relational Analysis (GRA), the definition of “Risk” (combination of likelihood and impact), and Hierarchical Task Analysis (HTA) to find several significant PSFs to prevent human errors. GRA deals with the incomplete and imperfect data; the definition of “Risk” provides a rational basis for ranking PSFs; and HTA gives support for considering the PSFs’ changes at different stages of a task. The proposed approach is tested on a real engineering case of oil tanker offloading work at offshore terminal. The result indicates that the method can be applied to identify key PSFs, which in turn provides recommendations for human error prevention to ensure the safety both on board and at terminal.

The Extension of Foundress Life Span and the Evolution of Eusociality in the Hymenoptera.

AbstractThe evolution of effectively sterile workers in the aculeate Hymenoptera (ants, bees, and stinging wasps) requires that a female's life span largely overlap that of her daughters. The evolution of long nest foundress life spans in eusocial species from the short life spans of solitary species is investigated. Analyses that control for phylogeny show for the first time that foundress adult life span increases and first-brood offspring development time decreases with increasing colony size, resulting in the ratio of foundress adult life span to worker total life span increasing with increasing colony size. These patterns support the hypothesis that the reproductive division of labor increases with increasing colony size, explaining the evolution of effectively sterile workers in species with large colonies. However, there is a discrete increase in foundress adult life span in the transition from noneusociality to eusociality that is independent of colony size. An analysis of life history characters suggests that this increase is explained by nests being founded by multiple females and progressive feeding of larvae as they develop. A reduced rate of senescence of a dominant cofoundress may be selected as a plastic response to social status if high-risk tasks performed by subordinate cofoundresses reduce the dominant's extrinsic mortality rate. Multiple-foundress nests in which one female is responsible for most or all of the reproduction (semisociality) and in which foundresses are full sisters are favored by haplodiploidy, perhaps explaining why eusociality is so common in the Hymenoptera.

Exploring uncertainty in regression neural networks for construction of prediction intervals

Deep learning has achieved impressive performance on many tasks in recent years. However, it has been found that it is still not enough for deep neural networks to provide only point estimates. For high-risk tasks, we need to assess the reliability of the model predictions. This requires us to quantify the uncertainty of model prediction and construct prediction intervals. One of the significant advantages of the proposed method is that it simultaneously implements point estimation and uncertainty quantification. In this paper, we explore the uncertainty in regression neural networks to construct the prediction intervals. In general, we comprehensively consider two categories of uncertainties: aleatory uncertainty and epistemic uncertainty. We design a novel loss function, which enables us to learn uncertainty without uncertainty labels. We only need to supervise the learning of regression tasks. In the process of training, the model implicitly learns aleatory uncertainty under the guidance of loss function. And that epistemic uncertainty is accounted for in the ensembled form. Our method correlates the construction of prediction intervals with uncertainty estimation. Experimental results on some publicly available datasets show that the performance of our method is competitive with other state-of-the-art methods.

Comparison between Direct Laryngoscopy with and without Aerosol Box for Intubation in Patients Undergoing General Anaesthesia during COVID-19 PandemicA Randomised Controlled Study

Introduction: The airway management of patients with COVID19 is a high risk task for anaesthesiologists. Several innovations have been born as a result of this problem, including aerosol boxes and clear plastic sheets. Aim: To compare the timing and attempt of direct laryngoscopy with and without aerosol box for intubation in patients undergoing general anaesthesia during the COVID-19 pandemic. Materials and Methods: This was a randomised controlled study was conducted in Parul Sevashram Hospital, Parul University, Vadodara, Gujrat, India from April 2021 to September 2021. A total of 50 patients were randomly divided into two equal groups as group A was intubated with an aerosol box and a macintosh laryngoscope, while group B was intubated with a clear plastic sheet and macintosh laryngoscope. With proper airway precautions and Personal Protective Equipments (PPE) comparative assessment of patients undergoing surgery in general anaesthesia was done. Time to intubate, number of attempts, ease of Endotracheal Tube (ETT) tube insertion, quality of Laryngoscopy view and Cormack Lehane scores were assessed in both the groups. Results: The mean time for intubation was high at 29.72 seconds in group A, while it was 23.16 seconds in group B; the difference was significant. Overall, 20 out of 25 (80%) patients could be intubated in 1st attempt in group B as compared to 15 out of 25 (60%) in group A. Airway visualisation using Percentage of Glottic Opening (POGO) scoring and Cormack Lehane staging were suggestive of better visualisation in group B than group A. Difficulties encountered during intubation like laryngoscopy, glottic visualisation, arm movement restriction, ETT negotiation, and stylet removal were lesser in group A as compared to group B. The incidence of complications like sore throat and airway bleeding were lower in group B as compared to group A. Conclusion: In the COVID-19 era, aerosol box and clear plastic sheets are effective barrier measures for airway management to prevent the anaesthesiologists from the aerosol transmission. But airway management with clear plastic sheet is technically easier than aerosol box.

Design and Manufacturing of a Fuze Disassembly Device

Dealing with ammunition fuzes is a high-risk task due to the inclusion of high sensitive explosive materials that may explode if not treated with care. In this work, the design and manufacturing of a disassembly device were explained. The device is based ona pneumatic system to improve safety in a process attached to highly sensitive explosive materials. The design was implemented using different manufacturing processes such as milling, lathe, and CNC machines in addition to welding. The disassembly operation was accomplished using a predetermined amount of air, and speed controlled from one hundred meters. The device is useful in production and R&amp;D processes as it saves the time of disassembly and reduces the explosion risk.

Working in a minefield; Nurses\u2019 strategies for handling medicine administration interruptions in\xa0hospitals, -a qualtiative interview study

BackgroundAdministering medicines is one of the most high-risk tasks in health care. However, nurses are frequently interrupted during medicine administration, which jeopardises patient safety. Few studies have examined nurses’ experiences and the strategies they adopt to cope with interruptions during medicine rounds. This paper identifies nurses’ strategies for handling and reducing interruptions and ensuring safety during medicine rounds, within the confines of the hospitals’ organisational systems.MethodsThis descriptive and exploratory research study was undertaken with experienced nurses in Norwegian hospitals in 2015 using semi-structured interviews. Interviews were designed to elicit experiences and strategies used for handling interruptions to medicine rounds. Data were analysed using qualitative content analysis based on inductive reasoning to identify meaningful subjects and reach an interpretive level of understanding regarding nurses’ experiences.ResultsAll 19 senior nurses who were approached were interviewed. From 644 condensed meaning units, we identified eight interpretative units and three themes: ‘working in environments of interruptions’, ‘personal coping strategies’, and ‘management-related strategies’. Nurses’ working environments were characterised by interruptions and distractions, which often threatened patient safety. To handle this unpredictability and maintain ward organisation, nurses developed their own personal strategies to overcome inherent problems with their working conditions, the absence of effective management, and colleagues’ reluctance to assume responsibility for minimising interruptions.ConclusionsAdministration of medicines in hospitals can be described as ‘working in a minefield’. Our findings indicate that the hospital management, in cooperation with nurses and other healthcare professionals, should take responsibility for improving the routine process of medicine administration by minimising avoidable interruptions. Patient safety can be improved when the hospital management takes steps to protect nurses’ work environments and assumes responsibility for resolving these challenges.

WITHDRAWN: Survey of intelligent surveillance system for monitoring international border security

Abstract One of the most difficult tasks in all countries is to monitor the security of international borders. Border security forces have no ability for monitoring long borders all the time and in all seasons. Robots can be used to detect intruders at the borders and then transmit the information to the control center. In hostile environment, various high-risk tasks are performed excellently via robots, whereas it might cause danger to soldiers. In this work, the major aim is to suggested a design for surveillance system in terms of monitoring the security of international borders. The suggested design consists of an automated vehicle with a monitoring system on the basis of face recognition and detection algorithms, the human’s presence is checked via the system which after that runs the algorithm for face recognition. In the case when the identified person’s face data doesn’t match the already-stored soldiers’ personal data, then the person will be recognized by the system as intruder and the system will send a message to the central control. Through the comparison tables for previous work, we suggest using HAAR algorithm for face detection and CNN algorithm for recognition by the use of raspberry pi and open-cv library. By studying a variety of monitoring technologies which involves the development of many robots with the use of raspberry-pi technology, the paper reports that all approaches are attempting to provide better results regarding the quality of video transmission and attempt to enhance the effectiveness with regard to the time spent on video transmission and face detection algorithm.

IMPLEMENTASI IDENTIFIKASI DAN TRACKING SASARAN SECARA OTOMATIS PADA ROBOT TEMPUR CIA VERSI N2MR3 DENGAN MENGGUNAKAN METODE PATTERN RECOGNITION

The ability of weaponry and technology in the world today has increased drastically where it is used to combat and defense, to improve the staff and material capabilities of Indonesia Military ranks, in general, the Army, which serves as state defense in protecting the territorial integrity of the Republic of Indonesia. Indonesia Army troops as the vanguard are always required to be professional in always carrying out high-risk tasks after the command over with high responsibility successfully. War (OMP) is an area that will be attacked or occupied by troops as a base and defended if the task still requires it. Activities carried out in operations such as issuing combat robots to find and track targets both during the day and at night automatically from threatening enemies. Therefore we need a tool that can help in carrying out tasks during the day and night. Tracking is the process of tracking and locking the target, which can be used to track and lock an object. One example of tracking is object tracking, security at night has both advantages and disadvantages, especially in conditions with very low light intensity. So night vision is needed to become a more effective security camera. Based on these problems, a study was made on bringing about automatic target identification and tracking on the N2MR3 a version of the CIA combat robot using the pattern recognition method to maximize security in conditions with very low intensity. The results of the study indicate that this study provides results in reading objects in light and dark conditions.

A physiology-based approach for estimation of mental fatigue levels with both high time resolution and high level of granularity

Abstract Mental fatigue (MF) monitoring is essential for eliminating accidents in high-risk tasks and providing better productivity management in daily work tasks involving human operators. Previous works only built MF monitoring systems with either high time resolution or high granularity level. We proposed a physiological-based approach to estimate MF Level every 2 seconds in a regression manner, a system that realized both high time resolution and high granularity level. The approach consists of an accurate MF level assessment method using the alignment score within a modified N-back task that is free from the lure effect, a long short-term memory (LSTM) deep learning framework, and a performance-reliability validation process. We used multiple physiological signals including ECG, respiration, and pupil diameter. As a result, we provided feasible estimating performance not worse than existing studies while realizing both high time resolution and high level of granularity. The interpretation analysis utilized accumulated local effects (ALE) to illustrate how black-box models make estimations and to improve the reliability of this approach.

Exploratory narrative text analysis to characterize tasks associated with injuries among electric utility line workers: EPRI Occupational Healthand Safety Database1995-2013.

Injury prevention is an important goal for electric utility line workers who are among the top 10 U.S. occupational groups sustaining fatal injuries on the job. Using narrative text fields, we identified 10 high-risk tasks among electric utility line workers. We performed a case-control study of task-injury associations using data from the Electric Power Research Institute Occupational Health and Safety Database (1995-2013). Drawn from 12,323 line worker injuries, cases were individuals with a major injury (5 or more days lost work) matched to controls, individuals with a minor injury (less than 1 day lost work), on company and year of injury. Conditional logistic regression estimated adjusted odds ratios (OR) and 95% confidence intervals (95% CI). Some tasks associated with higher odds for major injuries among line workers included: climbing up/down ladder/stairs/elevator (OR = 4.3; 95% CI = 2.6, 7.4); climbing down poles and transmission towers (OR = 5.5; 95% CI = 3.6, 8.4); entering/stepping out/approaching utility trucks, bucket, or vaults (OR = 5.8; 95% CI = 4.0, 8.4); and performing repetitive work/overtime (OR = 5.5; 95% CI = 3.2, 9.5). Knowledge gained can be used to focus efforts and plan efficient preventive measures that reduce injury rates, time lost from work, and costs within the electric power industry.

A New Metric Quantifying Chemical and Biological Property of Small Molecule Metabolites and Drugs.

One prominent class of drugs is chemical small molecules (CSMs), but the majority of CSMs are of very low druggable potential. Therefore, it is quite important to predict drug-related properties (druggable properties) for candidate CSMs. Currently, a number of druggable properties (e.g., logP and pKa) can be calculated by in silico methods; still the identification of druggable CSMs is a high-risk task, and new quantitative metrics for the druggable potential of CSMs are increasingly needed. Here, we present normalized bond energy (NBE), a new metric for the above purpose. By applying NBE to the DrugBank CSMs whose properties are largely known, we revealed that NBE is able to describe a number of critical druggable properties including logP, pKa, membrane permeability, blood–brain barrier penetration, and human intestinal absorption. Moreover, given that the human endogenous metabolites can serve as important resources for drug discovery, we applied NBE to the metabolites in the Human Metabolome Database. As a result, NBE showed a significant difference in metabolites from various body fluids and was correlated with some important properties, including melting point and water solubility.