Estimation of the uncertainty of metal content in a batch of waste printed circuit boards from computer motherboards.

Electronic wastes are a valuable resource due to their critical and precious metal content. To include these wastes in recycling or recovery chains, it is necessary to precisely determine their metal content. Because analysing the whole sample of a batch of electronic waste is not practical, different preparation and sampling or subsampling steps are necessary. Sampling induces an error in the composition of the final sample compared to that of the initial batch, which finally leads to uncertainty in the final metal content measurement as compared to the "actual" batch metal content. The aim was to characterize the uncertainty in metal content of a batch of 372 kg of WPCB. Thirty-nine metals were analysed and thirty-two were considered: base, precious, rare-earths and critical metals. An empirical method (i.e. replicated measurement tests) was thus applied, based on statistical calculations according to Eurachem Guidelines. Uncertainty arising during the 3 different stages of the preparation process (primary, secondly and tertiary sampling steps) was calculated. For the analysed given weight (0.5 g), the shredding efficiency, which directly affects metal particle size distribution, was found to be the most important factor influencing the uncertainty. Uncertainties in base metal content, which is often concentrated in the coarsest particles, arose mainly from the last preparation step (tertiary sampling). Conversely, precious metals and rare-earths were finely ground during the 3 preparation steps, which led to low uncertainties, despite their low concentration in the waste (<337 mg/t for precious and < 35 mg/t for rare-earths).

Hardware optimization for effective switching power reduction during data compression in GOLOMB rice coding.

Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. Golomb code is one of the effective technique for lossless data compression and it becomes valid only when the divisor can be expressed as power of two. This work aims to increase compression ratio by further encoding the unary part of the Golomb Rice (GR) code so as to decrease the amount of bits used, it mainly focuses on optimizing the hardware for encoding side. The algorithm was developed and coded in Verilog and simulated using Modelsim. This code was then synthesised in Cadence Encounter RTL Synthesiser. The modifications carried out show around 6% to 19% reduction in bits used for a linearly distributed data set. Worst-case delays have been reduced by 3% to 8%. Area reduction varies from 22% to 36% for different methods. Simulation for Power consumption shows nearly 7% reduction in switching power. This ideally suggest the usage of Golomb Rice coding technique for test vector compression and data computation for multiple data types, which should ideally have a geometrical distribution.

Low-Cost Non-Wearable Fall Detection System Implemented on a Single Board Computer for People in Need of Care.

This work aims at proposing an affordable, non-wearable system to detect falls of people in need of care. The proposal uses artificial vision based on deep learning techniques implemented on a Raspberry Pi4 4GB RAM with a High-Definition IR-CUT camera. The CNN architecture classifies detected people into five classes: fallen, crouching, sitting, standing, and lying down. When a fall is detected, the system sends an alert notification to mobile devices through the Telegram instant messaging platform. The system was evaluated considering real daily indoor activities under different conditions: outfit, lightning, and distance from camera. Results show a good trade-off between performance and cost of the system. Obtained performance metrics are: precision of 96.4%, specificity of 96.6%, accuracy of 94.8%, and sensitivity of 93.1%. Regarding privacy concerns, even though this system uses a camera, the video is not recorded or monitored by anyone, and pictures are only sent in case of fall detection. This work can contribute to reducing the fatal consequences of falls in people in need of care by providing them with prompt attention. Such a low-cost solution would be desirable, particularly in developing countries with limited or no medical alert systems and few resources.

The impact of increased computer screen time during the COVID-19 pandemic on the occurrence of upper part of musculoskeletal diseases among health personnel.

Health personnel who played a key role in the fight against the pandemic stayed during it burdened with increased working time using a computer. We analyzed the impact of increased computer working time during the COVID-19 pandemic on the occurrence of the upper part of musculoskeletal diseases among health personnel. The study group consisted of 418 health personnel, divided according to the time they worked at the computer during the pandemic: up to 2 h a day, from 3 to 5 h a day, and more than 6 h a day. The ICF profile analyzed symptoms of dysfunction of structures of the upper part of the musculoskeletal system (head and cervical spine, shoulder girdle, elbow joint, wrist joint). Employees working more than 6 h daily had a higher risk of developing restrictions in tone of isolated muscles and muscle groups p < 0.001), range of motion of the shoulder girdle (p < 0.001), increased tension of paraspinal muscles (p < 0.001), weakened shoulder girdle muscle strength (p < 0.001), elbow joint pain (p = 0.016), wrist joint pain (p < 0.001), coordination disorders (p = 0.004), difficulties in arm and hand use (p < 0.001), lifting and carrying objects (p = 0.008) and paraesthesia (p < 0.001) compared to those working less than 2 h daily. Additionally, working for 3-5 h and above 6 h compared to health personnel working up to 2 h was associated with a greater risk of headaches and cervical spine pain (p < 0.001), shoulder girdle pain (p < 0.001), limited mobility in the wrist joint (p = 0.003), and tremors (p < 0.001), that working below 2 h. Prolonged computer working time among health personnel during the COVID-19 pandemic is significantly associated with an increased risk of dysfunction and pain in structures of the upper part of the musculoskeletal system. Effective preventive measures are necessary to improve the functioning of the musculoskeletal system during extended periods of computer use.

A Work-Sampling Approach for Assessing Adequate Availability of Computers on Hospital Wards.

A key barrier to the use of health information technology on hospital wards is an insufficient number of computers to support clinical information system access. This paper reports on the development and pilot testing of a novel approach, based on work-sampling principles, for assessing adequate availability of computers.

[The use of gadgets in the student environment and their impact on the health of students].

In the information 21st century, almost everyone interacts with technical devices, including gadgets. A gadget is a small device designed to make life easier and better. Gadgets most often include smartphones, computers, tablets, headphones, smartphone speakers, smart watches and much more. In this article, we will focus on the most common gadgets - a smartphone and a computer, and also consider their effect on the student's body. The results of a social survey of students of the Admiral F. F. Ushakov State Maritime University on the indicator of smartphone screen time used and their well-being from this are presented. The authors show that the abuse of time spent in gadgets negatively affects the state of both physical and mental health of students. The article provides recommendations for a painless interaction.

Enhancing Monitoring Performance: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models.

In today's digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices.

Effects of head alignment devices on working memory and postural support during computer work.

The most common risk factor of computer workers is poor head and neck posture. Therefore, upright seated posture has been recommended repeatedly. However, maintaining an upright seated posture is challenging during computer work and induces various complaints, such as fatigue and discomfort, which can interfere working performance. Therefore, it is necessary to maintain an upright posture without complaints or intentional efforts during long-term computer work. Alignment devices are an appropriate maneuver to support postural control for maintaining head-neck orientation and reduce head weight. This study aimed to demonstrate the effects of workstations combined with alignment device on head-neck alignment, muscle properties, comfort and working memory ability in computer workers. Computer workers (n = 37) participated in a total of three sessions (upright computer (CPT_U), upright support computer (CPT_US), traction computer (CPT_T) workstations). The craniovertebral angle, muscles tone and stiffness, visual analog discomfort scale score, 2-back working memory performance, and electroencephalogram signals were measured. All three workstations had a substantial effect on maintaining head-neck alignment (p< 0.001), but only CPT_US showed significant improvement on psychological comfort (p = 0.04) and working memory performance (p = 0.024), which is consistent with an increase in delta power. CPT_U showed the increased beta 2 activity, discomfort, and false rates compared to CPT_US. CPT_T showed increased alpha and beta 2 activity and decreased delta activity, which are not conductive to working memory performance. In conclusion, CPT_US can effectively induce efficient neural oscillations without causing any discomfort by increasing delta and decreasing beta 2 activity for working memory tasks.

Association of digital device usage and dry eye disease in school children.

PURPOSE: To investigate the influence of digital device use (computers, laptops, tablets, smartphones) on dry eye disease (DED) in a pediatric population. SETTINGS AND DESIGN: This was a cross-sectional study. School children studying in grades 5-9 at two private schools in the city of Ahmedabad, the capital city of Gujarat, India were invited to participate in the study. METHODS: In this study, 462 children underwent ocular examination including tear film breakup time (TBUT) and Schirmer's test. Questionnaires were administered for collecting information on the type and duration of digital device usage separately for academic and leisure activities and the Ocular Surface Disease Index (OSDI) score. RESULTS: The mean age of participants was 11.2 + 1.4 years, and 63% were boys. The mean OSDI score was 37.2 + 11.8, and 90.5% had symptoms of DED. Children with moderate to severe DED (n = 88, 19%) had longer daily duration of device use and lower Schirmer's test and TBUT values compared to children with mild DED (P = 0.001). A cumulative exposure time of more than 3-3.5 h per day had a significantly increased risk of DED. Multivariable logistic regression analysis showed that increment in computer usage (odds ratio [OR] 1.94 for every half an hour increase, 95% confidence interval [CI] = 1.2-3.1) and children studying in higher grades (OR 1.30, 95% CI = 1.1-1.6) had a higher risk of moderate to severe dry eye. CONCLUSION: Cumulative device exposure time of more than 3-3.5 h per day had a significantly increased risk of pediatric DED. Children with an increment in computer usage by half an hour per day had a higher chance of experiencing moderate to severe dry eye. Policymakers should aim to restrict the screen time below 3 h on a daily basis.

On energy complexity of fully-connected layers.

The massive increase in the size of deep neural networks (DNNs) is accompanied by a significant increase in energy consumption of their hardware implementations which is critical for their widespread deployment in low-power mobile devices. In our previous work, an abstract hardware-independent model of energy complexity for convolutional neural networks (CNNs) has been proposed and experimentally validated. Based on this model, we provide a theoretical analysis of energy complexity related to the computation of a fully-connected layer when its inputs, outputs, and weights are transferred between two kinds of memories (DRAM and Buffer). First, we establish a general lower bound on this energy complexity. Then, we present two dataflows and calculate their energy costs to achieve the corresponding upper bounds. In the case of a partitioned Buffer, we prove by the weak duality theorem from linear programming that the lower and upper bounds coincide up to an additive constant, and therefore establish the optimal energy complexity. Finally, the asymptotically optimal quadratic energy complexity of fully-connected layers is experimentally validated by estimating their energy consumption on the Simba and Eyeriss hardware.

Daily Electronic Media Use and Sleep in Late Life.

OBJECTIVES: Daily electronic media use, including television viewing and computer use, is common in older adulthood. Yet, increased electronic media usage may disrupt nightly sleep, leading to sleeping fewer hours and more sleep disruptions. The current study examined these relationships in older adulthood, as well as the potential buffering effect of having a regular sleep schedule. METHODS: Older adults (N = 273) from the Daily Experiences and Well-Being Study (DEWS) completed 5-6 days of data collection where they answered questions at the beginning of the day about the previous night's sleep as well as questions throughout the day about daily electronic media use. They also wore Actical accelerometers to capture sleep regularity. RESULTS: Older adults reported sleeping fewer hours and having more sleep disturbances on days when they reported more instances of computer use. Sleep regularity moderated the daily association between TV viewing and sleep disturbances such that daily TV viewing was associated with more sleep complaints only for older adults who had less regular sleep patterns. However, sleep regularity no longer moderated this association when accounting for napping behavior. DISCUSSION: These findings provide evidence that older adults sleep worse after days when they engage in more electronic media use. The association with TV viewing with sleep disturbances on any given day is somewhat mitigated by engaging in regular sleep patterns. Researchers discuss the importance of assessing electronic media use and sleep in daily life as the role of sleep regularity may be a modifiable protective factor.

Unknotting tech neck by breaking the cycle of pain and disability: Comparing the impact of instrument assisted soft tissue mobilization on specific muscles and superficial back arm line.

BACKGROUND: Computer professionals often develop a forward head posture due to prolonged hours of computer use, leading to neck pain. Instrument-assisted soft tissue mobilization (IASTM), an advanced technique for treating myofascial trigger points, has become increasingly popular for addressing these musculoskeletal issues. OBJECTIVES: The study aimed to compare the effectiveness of IASTM mobilization on SBAL (superficial back arm line) and SM(specific muscles-upper trapezius, levator scapulae, and sternocleidomastoid) in managing chronic neck pain among computer professionals. PARTICIPANTS & METHODS: The study involved 62 computer professionals, randomly divided into two groups. Group A received IASTM on SBAL and group B received IASTM on SM for neck pain each receiving three sessions weekly for four weeks. Outcome variables like Neck Disability Index (NDI), NPRS(Neck Pain Rating Scale), Craniovertebral angle (CVA), and range of motion (ROM) for flexion, and side flexion (right & left side) were evaluated at baseline, 2 weeks and 4 weeks. RESULTS: Significant improvement in NPRS were observed in both the SBAL and SM groups after 2 weeks of IASTM, wth the SBAL group demonstrating greater improvement. At 4 weeks, IASTM on SBAL showed significantly higher improvements in NPRS, CVA, NDI, and flexion compared to the SM group. The repeated measures ANOVA indicated a significant main effect of both time and group, along with a significant interaction between time and group for all outcome variables, except for CVA. CONCLUSION: The study indicates that IASTM on SBAL may offer a more effective treatment for chronic neck pain in computer professionals compared to targeting specific muscles.

Traditional versus dynamic sitting: Lumbar spine kinematics and pain during computer work and activity guided tasks.

Dynamic sitting may mitigate low back pain during prolonged seated work. The current study compared pelvis and lumbar spine kinematics, pain, and work productivity, in traditional and dynamic sitting. Sixteen participants completed three 20-min blocks of computer work and activity guided tasks in a traditional office chair or backless and multiaxial rotating seat pan while kinematics were measured from accelerometers on the low back. Pain ratings were recorded on a visual analogue scale every 10 min. Similar pelvis and lumbar kinematics emerged when performing computer work in traditional and dynamic sitting. Pelvis and lumbar sagittal and frontal plane shifts and fidgets were largest for dynamic sitting in the activity guided tasks. Buttocks pain was higher in dynamic sitting, but low back pain and work productivity were unaffected. Dynamic sitting increased spine movement during activity guided tasks, without negatively impacting lumbar kinematics, low back pain, or productivity during seated computer work.

Orbicularis oculi muscle activity during computer reading under different degrees of artificially-induced aniseikonia.

Background: Aniseikonia is a binocular vision disorder that has been associated with asthenopic symptoms. However, asthenopia has been evaluated with subjective tests that make difficult to determine the level of aniseikonia. This study aims to objectively evaluate the impact of induced aniseikonia at different levels on visual fatigue by measuring the orbicularis oculi muscle activity in the dominant and non-dominant eyes while performing a reading task. Methods: Twenty-four collegiate students (24.00 ± 3.86 years) participated in this study. Participants read a passage for 7 minutes under four degrees of aniseikonia (0%, 3%, 5% and 10%) at 50 cm. Orbicularis oculi muscle activity of the dominant and non-dominant eye was recorded by surface electromyography. In addition, visual discomfort was assessed after each task by completing a questionnaire. Results: Orbicularis oculi muscle activity increased under induced aniseikonia (i.e., greater values for the 10% condition in comparison to 0%, and 3% conditions (p = 0.034 and p = 0.023, respectively)). No statistically significant differences were observed in orbicularis oculi muscle activity for the time on task and between the dominant and non-dominant eyes. Additionally, higher levels of subjective visual discomfort were observed for lower degrees of induced aniseikonia. Conclusion: Induced aniseikonia increases visual fatigue at high aniseikonia degrees as measured by the orbicularis oculi muscle activity, and at low degrees as measured with subjective questionnaires. These findings may be of relevance to better understand the visual symptomatology of aniseikonia.

Effects of 1-hour computer use on ulnar and median nerve conduction velocity and muscle activity in office workers.

OBJECTIVES: To compare the effects of 1-hour computer use on ulnar and median nerve conduction velocity and muscle activity in office workers with symptomatic neck pain and asymptomatic office workers. METHODS: A total of 40 participants, both male and female office workers, with symptomatic neck pain (n = 20) and asymptomatic (n = 20), were recruited. Pain intensity, ulnar nerve conduction velocity, median nerve conduction velocity, and muscle activity were determined before and after 1 hour of computer use. RESULTS: There was a significant increase in pain intensity in the neck area in both groups (P < .001). The symptomatic neck pain group revealed a significant decrease in the sensory nerve conduction velocity of the ulnar nerve (P = .008), whereas there was no difference in the median nerve conduction velocity (P > .05). Comparing before and after computer use, the symptomatic neck pain group had less activity of the semispinalis muscles and higher activity of the anterior scalene muscle than the asymptomatic group (P < .05). The trapezius and wrist extensor muscles showed no significant differences in either group (P > .05). CONCLUSIONS: This study found signs of neuromuscular deficit of the ulnar nerve, semispinalis muscle, and anterior scalene muscle after 1 hour of computer use among office workers with symptomatic neck pain, which may indicate the risk of neuromuscular impairment of the upper extremities. The recommendation of resting, and encouraging function and flexibility of the neuromuscular system after 1 hour of computer use should be considered.

Effects of computer use on upper limb musculoskeletal disorders and function in academicians.

Objectives. This study aimed to investigate the effects of academician's demographic characteristics and computer usage habits on upper limb musculoskeletal disorders (MSDs) and function. Methods. A cross-sectional observational study was conducted with 100 academicians. Data were collected using questionnaires, which included the patient-rated wrist evaluation questionnaire - Turkish version (PRWE-T), the Cornell musculoskeletal discomfort questionnaire - Turkish version (CMDQ-T), the upper extremity functional index - Turkish version (UEFI-T), demographic characteristics and average daily computer usage time. Results. A low-level significant correlation was found between the age of the individuals and the CMDQ-T forearm (p = 0.044; r = 0.202) and CMDQ-T wrist (p = 0.001; r = 0.337) scores. Women had higher CMDQ-T neck scores and lower UEFI-T scores than men (p < 0.05). Academicians who used computers for 6 h a day or more had higher PRWE-T and CMDQ-T neck, shoulder, upper arm and forearm scores, and had a lower UEFI-T score (p < 0.05). Conclusion. Neck, shoulder, upper arm and forearm symptoms were higher and upper extremity function was impaired in academicians who used computers for 6 h a day or more. Besides, gender and age were associated with upper limb MSDs and function, but occupation duration did not affect those outcomes in academicians.

A tree-based corpus annotated with Cyber-Syndrome, symptoms, and acupoints.

Prolonged and over-excessive interaction with cyberspace poses a threat to people's health and leads to the occurrence of Cyber-Syndrome, which covers not only physiological but also psychological disorders. This paper aims to create a tree-shaped gold-standard corpus that annotates the Cyber-Syndrome, clinical manifestations, and acupoints that can alleviate their symptoms or signs, designating this corpus as CS-A. In the CS-A corpus, this paper defines six entities and relations subject to annotation. There are 448 texts to annotate in total manually. After three rounds of updating the annotation guidelines, the inter-annotator agreement (IAA) improved significantly, resulting in a higher IAA score of 86.05%. The purpose of constructing CS-A corpus is to increase the popularity of Cyber-Syndrome and draw attention to its subtle impact on people's health. Meanwhile, annotated corpus promotes the development of natural language processing technology. Some model experiments can be implemented based on this corpus, such as optimizing and improving models for discontinuous entity recognition, nested entity recognition, etc. The CS-A corpus has been uploaded to figshare.

Using Computer Vision to Annotate Video-Recoded Direct Observation of Physical Behavior.

Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults (N = 26, aged 18-59 y) were recorded in their natural environment for two, 2- to 3-h sessions. Trained research assistants annotated videos using commercially available software including the following taxonomies: (1) sedentary versus non-sedentary (two classes); (2) activity type (four classes: sedentary, walking, running, and mixed movement); and (3) activity intensity (four classes: sedentary, light, moderate, and vigorous). Four machine learning approaches were trained and evaluated for each taxonomy. Models were trained on 80% of the videos, validated on 10%, and final accuracy is reported on the remaining 10% of the videos not used in training. Overall accuracy was as follows: 87.4% for Taxonomy 1, 63.1% for Taxonomy 2, and 68.6% for Taxonomy 3. This study shows it is possible to use computer vision to annotate aspects of physical behavior, speeding up the time and reducing labor required for direct observation. Future research should test these machine learning models on larger, independent datasets and take advantage of analysis of video fragments, rather than individual still images.