Background: Removable prostheses have seen a fundamental change recently because of advances in polymer materials, allowing improved durability and performance. Despite these advancements, notable differences still occur amongst various polymer materials and processing technologies, requiring a thorough grasp of their mechanical, physical, and therapeutic implications. Methods: Fours different processing techniques, all utilizing Polymethyl methacrylate (PMMA) as the main material, were used to construct complete dentures. The specimens underwent a mechanical test. Results: All the specimens broke under the influence of a certain force, rather than yielding through flow, as is characteristic for plastic materials. For each specimen, the maximum force (N) was recorded (figure 4), as well as the breaking energy, Conclusions: The best results were obtained in the case of the thermoplastic injection system and, surprisingly and contrary to those in the specialized literature, the worst results were recorded in the case of 3D printed samples.

The influence of growth temperature on the crystalline quality and optical properties of AlGaN at 325 nm, grown using plasma-assisted molecular beam epitaxy (MBE), was investigated. The growth temperature exhibited a significant impact on the growth mode and surface morphology. Specifically, at a growth temperature of 750 degrees Celsius, when appropriate Al and Ga flow rates were employed, the AlGaN layer exhibited exceptional surface morphology and favorable optical properties. The optical properties of the AlGaN material were explored, revealing the presence of two distinct non-radiative recombination centers characterized by different activation energies. The minority carrier lifetimes were measured as 560 ps at 10 K and 64 ps at room temperature. The underlying mechanism was analyzed from the perspective of carrier dynamics, shedding light on the observed phenomena.

The persistence of symptoms following COVID-19 infection represents a significant challenge in healthcare management. During the outbreak, tele-rehabilitation emerged as a new tool to support healthcare structures in providing rehabilitation services. This study assessed the effectiveness and the feasibility of a 3-week home-based motor and respiratory rehabilitation program for long COVID-19 individuals after traditional rehabilitation. Twenty-three patients completed the program and underwent functional tests at different time points (i.e. baseline, after hospital discharge, and after tele-rehabilitation). Motor function was evaluated using the instrumented Six-Minutes Walking Test (i6MWT), with monitored heart rate and oxygen saturation. Conversely, respiratory function was measured via forced vital capacity (FVC) and maximal voluntary ventilation (MVV) tests. Significant improvements (p < 0.05) in motor and respiratory function were observed throughout the intervention, including an 18.3% increase in walked distance from the baseline. The findings suggest that a home-based tele-rehabilitation can enhance motor and respiratory function in post-COVID patients. Despite limitations such as sample size and lack of control group, the positive outcomes seem to support the feasibility of the proposed tele-rehabilitation program in managing long COVID symptoms and promoting functional recovery. Further research is needed to validate these findings and explore tele-rehabilitation’s potential in broader patient populations.

A low-cost bio-mass-derived carbon substrate has been employed to synthesize MoS₂@carbon composites through a hydrothermal method. Carbon fibers derived from natural cotton provide a three-dimensional and open framework for the uniform growth of MoS₂ nanosheets, thus constructing hierarchically coaxial architecture. The unique structure could synergistically benefit fast Li-ion and electron transport from the conductive carbon scaffold and porous MoS₂ nanostructures. As a result, the MoS₂@carbon composites, when served as anodes for Li-ion batteries, exhibit a high reversible specific capacity of 820 mAh g^-1, high-rate capability (457 mAh g^-1 at 2 A g^-1), and excellent cycling stability. The superior electrochemical performance makes the MoS₂@carbon composites to be low-cost and promising anode materials for Li-ion batteries.

Asymmetric lithium salts, such as Lithium (difluoromethanesulfonyl)(trifluoromethanesulfonyl)imide (LiDFTFSI), have been demonstrated to surpass traditional symmetric lithium salts with improved Li+ conductivity and the capacity to generate stable solid electrolyte interphase (SEI) while maintaining compatibility with aluminum (Al0) current collector. However, the intrinsic reductive mechanism through which LiDFTFSI influences battery performance remains unclear and under debate. Herein, the detailed SEI reactions of LiDFTFSI-based electrolytes were investigated by combining density functional theory and molecular dynamics, aiming to clarify the formation process and atomic structure of SEI. Our results show that asymmetric DFTFSI– weakens the interaction between carbonate solvents and Li+, and substantially alter the solvation structure, exhibiting a well-balanced coordination capacity to bis(trifluoromethanesulfonyl)imide (TFSI–). Nanoseconds hybrid molecular dynamics simulation further reveals that the preferential decomposition of LiDFTFSI produces sufficient LiF and Li2O to facilitate a robust SEI. Moreover, the abundant F– generated from LiDFTFSI decomposition accumulates on the Al surface and subsequently combines with Al3+ from the current collector to form AlF3, potentially inhibiting corrosion of the current collector. Overall, these findings elucidate how LiDFTFSI regulates the solvation sheath and SEI structure, advancing the development of high-performance electrolytes compatible with current collector.

The decarbonization of the electricity grid as one among the actions to reduce fossil fuel emissions, and thus their impact on global warming in the future, will be achieved mainly via the integration and widespread diffusion of renewable power sources. This is going to be supported also by the shift from the paradigm production-transmission-distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will reduce the reliability of the grid very likely, unless appropriate countermeasures are taken/implemented, hopefully at the same time the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might determine in renewable-based power systems. This paper surveys the current state-of-the-art on anomalies and faults affecting wind-PV-storage hybrid power systems, i.e., the main renewable technology ensemble that will establish the future grid, and highlights possible research directions that may help to fill the literature gaps.

Это исследование сосредоточено на разработке системы искусственного зрения для гибкого дельта-робота-манипулятора и ее интеграции с межмашинной связью для оптимизации взаимодействия устройств в реальном времени. Эта интеграция направлена ​​на увеличение скорости роботизированной системы и улучшение ее общей производительности. Предлагаемое сочетание системы искусственного зрения с межмашинной связью позволяет обнаруживать и распознавать цели с высокой точностью в режиме реального времени в ограниченном пространстве, предназначенном для позиционирования, дальнейшей локализации и выполнения производственных процессов, таких как сборка или сортировка деталей. . В данном исследовании RGB-изображения используются в качестве входных данных для алгоритма MASK-R-CNN, а результаты обрабатываются в соответствии с особенностями прототипа дельта-манипулятора робота. Данные, полученные от MASK-R-CNN, адаптированы для использования в системе управления дельта-роботом с учетом ее уникальных характеристик и требований к позиционированию. Технология M2M позволяет манипулятору робота быстро реагировать на изменения, например, на перемещение объектов или изменение их положения, что имеет решающее значение для задач сортировки и упаковки. Система была протестирована в условиях, близких к реальным, чтобы оценить ее производительность и надежность.

Three-dimensional V₂O₅ hollow structures have been prepared through a simple synthesis strategy combining solvothermal treatment and a subsequent thermal annealing. The V₂O₅ materials are composed of microspheres 2–3 μm in diameter and with a distinct hollow interior. The as-synthesized V₂O₅ hollow microspheres, when evaluated as a cathode material for lithium-ion batteries, can deliver a specific capacity as high as 273 mAh·g⁻¹ at 0.2 C. Benefiting from the hollow structures that afford fast electrolyte transport and volume accommodation, the V₂O₅ cathode also exhibits a superior rate capability and excellent cycling stability. The good Li-ion storage performance demonstrates the great potential of this unique V₂O₅ hollow material as a high-performance cathode for lithium-ion batteries.

This study explored differences in student-athletes’ symptoms of depression, anxiety, and stress pre- to post-COVID-19 pandemic. The WHO reported a 25% increase in depression and anxiety rates worldwide with young people disproportionately affected. Student-athletes face many stressors related to their sporting and academic feats but what is not known is how the COVID-19 pandemic affected their experiences of mental illness. A multiple cohort cross-sectional study design was employed, and data collected using physical and online surveys. Participants (M age = 19.98 years, SD = 1.50) were recruited from UK universities (N = 807; 427 pre-pandemic cohort, 380 post-pandemic cohort). Results revealed statistically significant differences in mean depression, anxiety, and stress scores between cohorts. Scores for the post-pandemic cohort were significantly higher than pre-pandemic, suggesting a worsening of symptom severity. Distributions of student-athletes across categories of symptom severity also worsened for depressive and anxiety symptoms post-pandemic and were skewed towards more severe categories. Symptoms of de-pression, anxiety, and stress were a concern pre-pandemic. Rates are higher in the post-pandemic cohort, suggesting a worsening of symptoms. This data adds to evidence on student-athletes’ symptoms of mental illness by exploring a UK sample and comparing scores pre- and post-pandemic.

A low cost, scalable and robust process has been proposed as innovative method for coating solar mirrors with a self-cleaning, transparent in the full solar range and versatile material based on auxetic aluminium nitrides, previously obtained on the laboratory scale. This work presents the scaling-up of fabrication process from laboratory to prototypal scale and pre-liminary results of outdoor self-cleaning solar mirror prototypes testing in the demonstrative CSP plant ENEASHIP located in Casaccia (Rome) ENEA research centre. Prototypes 50x40cm have shown stability in external conditions: no degradation occurs during the test campaign. Their washing restores the initial reflectance affected by soiling and the self-cleaning performance allows the utilization of a reduced quantity of water for cleaning operations with respect to uncoated glass of back surface mirrors. A similar self-cleaning AlN coating could be utilized on other solar components affected by soiling, such as glass envelopes of heat collecting elements, PV panels and everywhere are required self-cleaning performance combined to optical ones.

Phased injection of natural gas into internal combustion marine engines is a promising solution for optimizing performance and reducing harmful emissions, particularly unburned methane, a potent greenhouse gas. This innovative practice distinguishes itself from continuous injection because it allows for more precise control of the combustion process with only a slight increase in system complexity. By synchronizing the injection of natural gas with the intake and exhaust valve opening and closing times while also considering the gas path in the manifolds, methane release into the atmosphere is significantly reduced, making a substantial contribution to efforts to address climate change. Moreover, phased injection improves the efficiency of marine engines, resulting in reduced overall fuel consumption, lower fuel costs, and increased ship autonomy. This technology has been tested on a single-cylinder, large-bore, four-stroke research engine designed for marine applications, operating in dual-fuel mode with diesel and natural gas. Performance was compared with that of the conventional continuous feeding method. Evaluation of the effect on equivalent CO2 emissions indicates a potential reduction of up to approximately 20%. This reduction effectively brings greenhouse gas emissions below those of the diesel baseline case, especially when injection control is combined with supercharging control to optimize the air-fuel ratio.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

Achieving the thermal conductivity required for efficient heat management in semiconductors and other devices requires the integration of thermally conductive ceramic fillers at concentrations of 60 vol% or higher. However, the increased filler content often negatively affects the mechanical properties of the composite matrix, limiting its practical applicability. To address this issue, in this paper, we present a new strategy to reduce the required ceramic filler content: the use of a thermally conductive ceramic composite filler with carbon nanotubes (CNT) grown on aluminum nitride (AlN). We have combined catalyst coating technology with vacuum filtration to ensure that the catalyst is uniformly applied to micrometer-sized AlN particles, followed by efficient and uniform synthesis of CNTs using a water-assisted process in a vertical furnace. By carefully controlling the number of vacuum filtration cycles and the growth time of the CNTs, we demonstrate precise control over the number and length of the CNT layers, thereby adjusting the properties of the composite to the intended specifications. When AlN/CNT hybrid fillers are incorporated into silicone rubber, while maintaining the mechanical properties of rubber, the thermal diffusivity achieved at reduced filler levels exceeds that of composites using AlN-only or simultaneous AlN and CNTs formulations. This demonstrates the critical role of CNTs on AlN surfaces. Our study represents a significant advancement in the design of thermally conductive materials with potential implications for a wide range of applications.

In this study, the mechanical and piezoresistive properties of mortars reinforced with carbon nanotubes (CNTs) and carbon micro fibers (CMFs) are determined. Silica fume as well as a polymer with polyalkylene glycol graft chains were used as dispersant agents for the CNTs and CMFs incorporation into the cement paste. The mechanical properties of the mortar composites were investigated in respect to their flexural and compressive strength. A four-probe method was used for the estimation of their piezoresistive response. The test outcomes revealed that the combination of the dispersant agents along with a low content of CNTs and CMF by weight of cement (bwoc) result in the production of a stronger mortar with enhanced mechanical performance and durability. More specifically, there was an increase in flexural and compressive strength up to 38% and 88%, respectively. Moreover, the piezoresistive response of the reinforced cementitious materials was attributed to the variation of electrical resistivity due to tunneling transport of electrons stemming from two different mechanisms due to the incorporation of the conductive admixtures (CNTs and CMF). This work aims to contribute to the progress acceleration in the field of developing structural materials with self-sensing and electrical actuation related properties.

Background Patients with severe dysphagia are usually fed using a nasogastric tube (NGT). Many patients who receive long-term NGT feeding are unable to obtain sufficient nutrients orally immediately after NGT removal. Thus, a transitional period involving oral diet training is required to transition from NGT feeding to exclusive oral feeding. We aimed to investigate the therapeutic effect of oral diet training in indwelling NGT patients with prolonged dysphagia. Methods A total of 175 patients who were fed using an NGT for more than 4 weeks were enrolled. Their swallowing function was evaluated by a videofluoroscopic swallowing study (VFSS). During the VFSS, patients received thick and thin barium while the NGT was insulted. Then, the patients underwent a VFSS without an NGT thirty minutes after NGT removal. If a patient had no aspiration with NGT inserted during the VFSS, oral diet training combined with NGT feeding was recommended. Results Of the 49 indwelling NGT patients who were recommended to receive oral diet training, 39 (79.6%) transitioned to exclusive oral feeding. A transition period of 2-8 weeks was required for them to achieve full oral feeding. Patients who were eligible for oral feeding trials showed no significant aspiration during the VFSS with an NGT inserted and had sufficient cough function. Patients who required prolonged NGT feeding and who could not complete oral trials showed significant aspiration during the VFSS when an NGT was inserted. Conclusion This study demonstrated that oral diet training combined with NGT feeding was safe in patients with prolonged dysphagia who had sufficient cough function and no aspiration during VFSS. We suggest that if the patient is a proper candidate for NGT removal, direct oral feeding training with an NGT inserted could be a useful therapeutic strategy during the transitional period from long-term NGT feeding to successful oral feeding.

This research aims to apply Artificial Intelligence (AI) methods, specifically Artificial Immune Systems (AIS), to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The motivation for using these variations of PID controllers is that they are functionally implemented in modern industrial controllers, where they provide precise process control. The research results in a novel solution to the control synthesis problem for the industrial system. In particular, the research deals with the synthesis of I-P control for a two-loop system in the technological process of a distillation column. This synthesis is carried out using the AIS algorithm, which is the first application of this technique in this specific context. Methodological approaches are proposed to improve the performance of industrial multivariable control systems by effectively using optimization algorithms and establishing modified quality criteria. The numerical performance index ISE justifies the effectiveness of the AIS-based controllers in comparison with conventional PID controllers (ISE₁ = 1.865, ISE₂ = 1.56). The problem of synthesis of the multi-input multi-output (MIMO) control system is solved, considering the interconnections due to the decoupling procedure.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

The multiphase motors due to their reliability find new areas of application in the industry and transportation. Developing of any system with multiphase motors at first requires elaborate model of the system, define control mode and controllers. The model of control system for the six-phase drive is based on its conventional d-q mathematical model and indirect field-oriented control. Simulink model of six-phase drive with indirect field-oriented control and simulated with two types PID and PID fuzzy controllers. The simulation results, reflecting step response and provided speed reference law as well as load application at constant speed, are presented and analyzed. Fuzzy controller with 49 rules is considered and applied. Six-phase electric drive with field-oriented control at well-tuned PID controller has good step response specifications: settling time 0.13 s at starting without load, step response has no overshoot, small about 1 % short time decrease of speed at load of 5 Nm application. The system employing PID fuzzy controller shows slightly better results in response to torque application: the decrease in speed 1 % is eliminated in shorter time. Simulation results indicate the robustness of both systems with six-phase drive to disturbance and provide high quality transient specifications at determined reference speed.

Plant-derived compounds have been historically used for the treatment of various diseases and an important source of several clinically useful anti-cancer agents. The Artemisia annua is known as a medicinal herb that is effective against cancer. Thus, the purpose of this study was to investigate whether ethanol extracts of Artemisia annua could increase an anti-cancer effect against LS174T cells. Our results show that Artemisia annua extracts (AAE) induces apoptosis through the regulation of mitochondria outer membrane potential. We demonstrate that apoptosis is induced by p53-dependent manner when cells were treated with Nutlin-3 (MDM2 inhibitor) and LY294002 (Akt inhibitor). In addition, the apoptotic effect was also observed in the p53-independent manner through pifithrin-α (p53 inhibitor) and celecoxib (COX-2 inhibitor) treatment. As in in vitro, AAE induced apoptotic effects and regulated apoptosis-related proteins in LS174T xenograft model. Taken together, these results suggest that Artemisia annua extracts exerts an anti-cancer effect through p53-independent manner, as well as p53-dependent manner by p53´ translocation in LS174T colon cancer cells.

Currently, there is a high interest in smart sensors and integrated composite materials in various industries such as construction, aviation, automobile, medical, information technology, communication, and manufacturing. Here, a new conceptual design for a force and temperature sensor system is developed by integrating fiber optic Bragg grating sensors embedded within composite materials, and a mathematical model is proposed that allows one to estimate strain and temperature based on signals obtained from optical Bragg gratings. This is important for understanding the behaviors of sensors under different conditions and for creating effective monitoring systems. Describing the strain gradient distribution, especially considering different materials with different values of Young's modulus, provides insight into how different materials respond to applied forces and temperature changes. The shape of the strain gradient distribution was obtained, which is a quadratic function with a maximum value of 1500 µ, with a maximum value at the center of the lattice and a symmetrically decreasing strain value with distance from the central part of the fiber Bragg grating. With axial strain at the installation site of the Bragg grating sensor under applied force values ranging from 10 to 11 N, the change in strain was linear. As a result of theoretical research, it was found that the developed system with fiber-optic sensors based on Bragg gratings embedded in composite materials is resistant to external influences and temperature changes.

Background: Gender differences characterize the prevalence and attitudes toward weight management. Despite limited evidence suggesting greater weight loss in women with anti-obesity pharmacotherapy, gender-specific analysis remains underexplored. This retrospective study aimed to evaluate the gender-specific response to liraglutide 3.0 mg treatment in people with obesity without type 2 diabetes (T2D). Methods: Data were collected from 47 patients (31 women, 16 men), with age > 18 years, BMI ≥ 30 kg/m^2, absence of T2D, and with exclusion of prior anti-obesity treatment, comorbidities, or bariatric surgery. Only patients who maintained the liraglutide 3.0 mg dose for at least 6 months were included. Results: Both sexes showed significant reductions in weight and BMI at 3 and 6 months. Men achieved greater weight loss (WL), BMI reduction, % WL, WL >5% and >10% than women, and they also showed more significant improvements in metabolic parameters (total and LDL cholesterol, Fibrosis-4 Index FIB-4). No significant gender differences were observed in glucose metabolism or renal function. Conclusion: This study showed a greater therapeutic effect of liraglutide 3.0 mg in men. Given men's higher risk of cardiovascular disease (CVD), and underrepresentation in clinical weight loss programs, these findings may increase male engagement and improve their CVD risk.

Cerebral palsy (CP) presents challenges in movement and posture due to central nervous system damage, necessitating lifelong management. The COVID-19 pandemic has limited access to traditional treatments, increasing demand for home-based therapies and digital resources. This study analyzed the qualitative and quantitative aspects of YouTube videos focusing on CP therapy for children. In total, 95 videos were evaluated for content quality, therapeutic program efficacy, and popularity. Most videos were uploaded by health professionals. Neuromotor therapy types varied, with Bobath therapy being prominent. The quality assessments revealed moderate to high reliability in video content; nonetheless, shortcomings in addressing uncertainties were observed. Therapeutic programs often lack crucial details such as intensity and outcome assessment methods. No significant differences were found between professional and non-professional uploads. Classifiable videos based on exercise type, in contrast to the types of neuromotor therapy, exhibited better therapeutic quality than unclassified ones. Notably, video popularity did not correlate with quality. This study underscores the need for improved educational content on YouTube, particularly in providing comprehensive therapeutic information for CP management. Further refinement of evaluation tools is essential for assessing the rationale behind neuromotor therapeutic modalities for children with CP.

(1) Background: This study aims to investigate differences in the perspectives of exercise intensity for resistance exercise of healthcare professionals (HCPs) and adults living with spinal cord injury (SCI) in Asian countries. Also, it verifies the appropriateness of the conventional indicators for resistance exercise intensity in an Asian context; (2) Methods: 121 certified HCPs and 107 adults with SCI participated in a survey conducted in Indonesian, Japanese, Korean and Thai languages. The survey asked what principles are important for prescribing resistance exercise, and if ‘moderate’ and ‘vigorous’ were appropriate terms for indicating an intensity level. Perspective differences were examined between both groups and, further, between countries from a psychosocial perspective; (3) Results: Of the HCPs, 92% perceived that resistance exercise intensity was far more important than frequency, time or type of exercise while frequency (61%) followed by intensity (58%) was perceived as important by respondents with SCI. Only Japanese respondents with SCI agreed with HCP’s perception of intensity. Both HCPs and respondents with SCI showed unfavorable responses on the usage of ‘moderate’ and ‘vigorous’ for describing resistance exercise intensity, especially Japanese HCP and respondents with SCI from Indonesia; (4) Conclusion: Our findings suggest that non-HCPs must improve their perceptions of exercise intensity. Findings also suggest the need for more explicit and user-friendly terms or expressions indicating the level of intensity in a local (Asian) context.

Yttria stabilized zirconia materials are frequently used in mechanical engineering and biomedical applications. Demanding loading conditions require materials combining a high level of strength and fracture toughness. A ready to press alumina doped 2 mol% yttria stabilized zirconia powder was shaped by axial pressing and sintering in air at 1250 - 1500 °C for 2 h. The best combination of strength and toughness value of 7.8 MPa√m. Materials sintered in the middle of the chosen temperature range combine fully density, high transformability and small grain size. Toughness measurements by direct crack length measurements delivered unrealistically high fracture toughness values.

With continuous increase in integration of the semiconductor devices, the requirements on size accuracy and surface quality of the etched lead frame are higher. The etchant is a key factor on etching process and etched surface quality, while effects of the difference in etchants on microscopic etched surface morphology of the Cu alloy have not been directly studied. In this study, the aqua regia, acidic FeCl3 and CuCl2 solutions were used as etchants, and the CuCrSn alloy specimens with different microstructure were etched and characterized. The results show that the etching rate in aqua regia is high, and the grain orientation, grain boundary (GB) and dislocation have significant influence on the local etching rate. The preferential etching of some atomic planes forms steps between grains with different orientations, and the preferential etching around the GB and dislocation group forms grooves, resulting in high surface roughness. For etching of the FeCl3 and CuCl2 etchants, the steps and grooves become blurred, and almost invisible on the surface etched by the CuCl2 etchant, thus the surface roughness is much lower. The CuCrSn alloy surface etched by the aqua regia is clean, with little Cr-rich particles, while high density Cr-rich particles are remained on the surfaces etched with the FeCl3 and CuCl2 etchants. For the same kind of etchant, the ion concentration can affect the etching mechanism and etching surface morphology.

Objective: This study aimed to explore potential interventions or preventive measures to reduce the incidence of falls among the elderly in the Madinah region, and to provide recommendations for healthcare professionals, policymakers, and community stakeholders to enhance fall prevention strategies and improve the overall well-being of the elderly population in this area. Methods: The study adopted a cross-sectional study design, and was carried out in the Madinah region using convenient physical assignment among of Saudi and non-Saudi local residents aged 65 years and over. Questionnaire surveys were circulated and demographic characteristics were collected, including participants’ fall experience and health status data over the past year, in order to assess the incidence of falls among the elderly population. This design allows for the collection of data at a single point in time, aiming to evaluate the prevalence, risk factors, and consequences of falls among the target population. Results: A total of 189 participants were recruited to the study, and the data indicated that the prevalence of falls among those aged 65 years and older in the past year was 41.5%. We found that 73% of the participants had experienced falls and post-fall injuries within the past 12 months. There were minimal differences in terms of physical handicapped cases found between those with and without a fall history. Logistic regression analysis revealed a significant link between fall experience and maximum bite force. Conclusion: There is a high prevalence of falls in the elderly population in the Madinah region. Our study reveals that 50.79% of our elderly participants had experienced more than one fall during the preceding year. Some preventable risk factors were identified in an analysis of their daily routines. The impacts of these risk factors have potential consequences for their healthy lives.

Neuro-rehabilitation services are essential in reducing post-stroke impairments, enhancing independence, and improving recovery in hospital and post-discharge. However these services are therapist-dependent and resource intensive. Patients’ disengagement and boredom in stroke units are common which adversely affect functional and psychological outcomes. Novel techniques such as use of iPads™ are increasingly researched to overcome such challenges. The aim of this review is to determine the feasibility, effectiveness, acceptability, and barriers to the use of iPads™ in stroke neuro-rehabilitation. Four databases and manual literature search were used to identify published studies using the terms “iPad”, “Stroke”, and “neuro-rehabilitation”. Studies were included in accordance with the review selection criteria. A total of 16 articles were included in the review. The majority of the studies focused on iPads use in speech and language therapy. Although of small scale, the studies highlighted that iPads are feasible, have the potential to improve rehabilitation outcomes, and can improve patient’s social isolation. Patients’ stroke severity and financial limitations are some of the barriers highlighted in this review. This review presents preliminary data supportive for the use of iPad technology in stroke neuro-rehabilitation. However, further research is needed to determine impact on rehabilitation goals acquisition, clinical efficacy, and cost-efficiency.

Bio-molecular reagents like antibodies required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Bio-Resource Exchange that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, in order to index donations outside of our portal, we mined scientific articles to find instances of donations of antibodies and attempted to extract information about these donations at the finest granularity. Specifically, we extracted the name of the donor, his/her affiliation and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we propose two approaches – a rule based algorithm and a bootstrapped relation learning algorithm. The algorithms extracted donor names, affiliations and antibody names with average accuracies of 57% and 62% respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future. Contact: [email protected], [email protected] Database URL: http://tonks.dbmi.pitt.edu/brx Supplementary information: Supplementary data are available at Database online.

Omega-3 polyunsaturated fatty acids (ɷ-3 PUFAs) offer diverse health benefits, such as supporting cardiovascular health, improving cognitive function, promoting joint and musculoskeletal health, and contributing to healthy aging. Despite their advantages, challenges like oxidation susceptibility, low bioavailability, and potential adverse effects at high doses persist. Nanoparticle encapsulation emerges as a promising avenue to address these limitations while preserving stability, enhanced bioavailability, and controlled release. This comprehensive review explores the therapeutic roles of omega-3 fatty acids, critically appraising their shortcomings and delving into modern encapsulation strategies. Furthermore, it explores the potential advantages of Metal-organic framework nanoparticles (MOF NPs) compared to other commonly utilized nanoparticles in improving the therapeutic effectiveness of omega-3 fatty acids within drug delivery systems (DDS). Additionally, it outlines future research directions to fully exploit the therapeutic benefits of these encapsulated omega-3 formulations for cardiovascular disease treatment.

As the global population faces an increasing prevalence of physical disabilities, the challenge of motor rehabilitation has become increasingly urgent. Considering that approximately 15% of the global population lives with some form of disability, with a significant number having motor disa-bilities that greatly impact their quality of life, the integration of robotic technologies into rehabili-tation processes offers an opportunity for significant improvement in functional recovery and pa-tient autonomy. This analysis aims to evaluate the progress and challenges encountered in imple-menting robotic technology for the rehabilitation of patients with physical disabilities, highlighting the potential of emerging technologies such as exoskeletons, assisted training devices, and brain-computer interface systems in facilitating motor recovery. By examining relevant clinical studies, the analysis demonstrates the varied effectiveness of robotic therapies compared to traditional re-habilitation methods, while also highlighting the need for ongoing research to optimize care prac-tices and address challenges related to cost, accessibility, and treatment personalization. In con-clusion, robotic technologies represent a promising direction in motor rehabilitation, with the po-tential to revolutionize the care of patients with physical disabilities, significantly improving quali-ty of life and social integration.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system's safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system's safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Specifically leveraging the Lyapunov function, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.

Glucose is a simple sugar molecule. The sugar molecule is chemically symbolised as C6H12O6. This means that the glucose molecule contains 6 carbon atoms (C), 12 hydrogen atoms (H) and 6 oxygen atoms (O). In human blood, the molecule glucose circulates as blood sugar. Normally after eating or drinking, our body breaks down the sugars in food and uses them to obtain energy for our cells. To do this, our pancreas produces insulin. Insulin "pulls" sugar from the blood and puts it into the cells for use. If someone has diabetes, their pancreas can't produce enough insulin. As a result, the level of glucose in the blood rises. This can lead one to many potential complications, including blindness, disease, nerve damage, amputation, stroke, heart attack and damage to blood vessels etc. In this study, a non-invasive method for monitoring blood glucose has been developed and is therefore easily usable. The device developed can measure the glucose level continuously and eliminates the disadvantages of the invasive system. Near-IR sensors (optical sensors) were used to estimate the concentration of glucose in the blood; these sensors have a wavelength of 940 nm. The sensor is placed on the tip of the finger and the output of the optical sensor is then connected to the microcontroller at the analogue input. Another sensor used, but only to provide more medical information was the heartbeat sensor. After processing and regression analysis, the glucose level is predicted, and data is sent via the Bluetooth network to a developed APP. The results of the implemented device were compared with available invasive methods. The study consists of a hardware platform inserted into an armband and then a small black parallelepiped-shaped box in which the optical sensors are inserted. The hardware consists of a microcontroller, a Near-IR optical sensor, a heartbeat sensor and a Bluetooth module.

Quasi-solid composited electrolytes were produced by LiFSI initiated polymerized poly-(Dioxolane) (PDOL) with garnet-LLZO (Li7La2Zr3O12) nanofibers and used in lithium-metal batteries. Linear sweep voltammetry shows that the initial oxidation voltage of 4.6 V for Gel-10% (G10, 10% wt. LLZO in PDOL) vs Li/Li+. EIS test of stainless steel// stainless steel symmetric cells for Gel-1% (G1), Gel-5% (G5) and Gel-10% (G10) show ion conductivity (5*10-5 S/cm). Li/G10/Li symmetric cells have stable stripping-plating cycling performance over 1300 hours continuously with the current density of 0.1 mA/cm2 at 25 oC, indicating a good interface contact and stable lithium-ion transport pathway through the composite electrolytes. LFP/G10/Li batteries have stable charge-discharge plateau, low polarization voltage of 0.07-volt, energy efficiency of 95% and coulombic efficiency of ~99% at current density of 26.3 mA/g at 25 oC for over 100 cycles.

This study aimed to investigate the efficacy of manual therapy and exercise versus synchronized telerehabilitation with self-manual therapy and exercise in treating Subacromial Pain Syndrome (SAPS). Sixty individuals diagnosed with SPS, aged 18-50 years, were randomly assigned to home exercise (HE), manual therapy (MT), and telerehabilitation (TR) groups. Treatment protocols were administered over 8 weeks and included specific exercises and therapy interventions. Out-come measures included Visual Pain Scale (VAS), shoulder range of motion (ROM) via gonio-metric measurements, Quick Disability Arm-Shoulder-Hand Problems Survey (Q-DASH), and patient satisfaction. Results revealed that both MT and TR groups exhibited reduced pain, in-creased ROM, lower Q-DASH scores, and higher patient satisfaction compared to the HE group. However, no significant differences were found between MT and TR groups in pain levels, ROM, Q-DASH scores, or patient satisfaction. The study concludes that both telerehabilitation and manual therapy effectively alleviate pain and are well-received by patients with SPS. Addition-ally, manual therapy demonstrates superiority in enhancing functional levels compared to exer-cise-based interventions. (Registration: NCT05200130)

Nowadays, the study of preparing silica/rubber composites, which can be used in “green tire”, in energy saving method is fast-growing. In our work, silica modified by using alcohol polyoxyethylene ether (AEO) and 3-Mercaptopropyltriethoxysilane (K-MEPTS) together were investigated. Thermal gravimetric analyzer (TGA) result indicated that both AEO and K-MEPTS could be grafted on the silica surface. Raman spectroscopy confirmed that the AEO could generate a certain steric hindrance for the mercaptopropyl group on K-MEPTS. Silica modified by using AEO and K-MEPTS together can be completely co-coagulated with the rubber in preparing silica/natural rubber (NR) composites by latex compounding method. AEO can form a physical interface between silica and rubber; meanwhile, K-MEPTS can form a chemical interface between silica and rubber. The effects of chemical and physical interface between silica and rubber on dynamic and mechanical performances of silica/NR composites were also given in this research. A proper combination of physical and chemical interface between silica and NR can improve performances of silica/NR composites.

The purpose of this paper is to explore the implementation of Retrieval-Augmented Gen- 14 eration (RAG) technology with open-source Large Language Models (LLMs). For this purpose, a 15 web-based application named PaSSER that integrates RAG with three models - Mistral:7b, 16 Llama2:7b: Orca2:7b, is developed. In its development various technologies are used, including 17 Blockchain for managing and storing assessment results of built-in testing modules. PaSSER em- 18 ploys a set of evaluation metrics for LLMs’ performance - METEOR, ROUGE, BLEU, Perplexity, 19 Cosine similarity, Pearson correlation, and F1 score. The specialized knowledge base is in the smart 20 agriculture domain. As an illustration, the paper presents results and analysis of two tests. First one 21 is assessing the performance of LLMs across different hardware configurations, the other is deter- 22 mining the model that delivers most accurate and contextually relevant responses within the RAG. 23 The paper also discusses the integration of blockchain technology with LLMs to manage and store 24 assessment results within a blockchain environment. The tests revealed that Mistral:7b demon- 25 strates the best results across most of the evaluated metrics. The discussion section comments on 26 technical and hardware considerations that affect the performance of LLMs. Future development 27 will focus in leveraging new larger pre-trained open-source LLMs, exploring different finetuning 28 approaches, and further integration with Blockchain and IPFS.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

In clinical practice, patient assessments rely on established scales. Integrating data from these scales into the International Classification of Functioning, Disability, and Health (ICF) framework has been suggested; however, a standardized approach is lacking. Herein, we tested a new approach to develop a conversion table translating clinical scale scores into ICF Qualifiers based on a clinician survey. The survey queried rehabilitation professionals about which functional independence measure (FIM) item scores (1-7) corresponded to the ICF qualifiers (0-4). A total of 458 rehabilitation professionals participated. The survey findings indicated a general consensus on the equivalence of FIM scores with ICF qualifiers. The median value for each item remained consistent across all item groups. Specifically, FIM 1 had a median value of 4; FIM 2 and 3 had median values of 3; FIM 4 and 5 had median values of 2; FIM 6 had a median value of 1; and FIM 7 had a median value of 0. Despite limitations due to the irreconcilable differences between the frameworks of existing scales and the ICF, these results underline the ICF's potential to serve as a central hub for integrating clinical data from various scales.

Purpose To investigate the effects of intra-articular glenohumeral joint triamcinolone injection in treating secondary adhesive capsulitis after breast cancer surgery.Methods This study prospectively enrolled 37 participants, 22 of the breast cancer surgery group and 15 of the idiopathic group. All participants received intra-articular glenohumeral joint injection in the affected shoulder joint and received physiotherapy for 12 week. The clinical assessment evaluated before, and 1, 3, and 6 months after the intervention. The primary outcome was the change in the Shoulder Pain and Disability Index (SPADI).Results The total SPADI(%) in both groups showed significant decrease from baseline to 1, 3, and 6 months after the intervention. There was no significant difference in the differences of the total SPADI from baseline to 1, 3, and 6 months between two groups. However, the improvement in the SPADI pain subscale and the PROM of abduction at 3 and 6 months post-injection in the breast cancer group were inferior to the idiopathic group.Conclusion Intra-articular triamcinolone injection for adhesive capsulitis after breast cancer surgery significantly improved in significant improvements of pain, shoulder function and the PROM. The effects were similar to idiopathic adhesive capsulitis, but in the breast cancer surgery group, appropriate pain and ROM management and monitoring are required even 3 months after injection. With a standardized injection protocol, intra-articular triamcinolone injection could be an effective and safe treatment option for secondary adhesive capsulitis after breast cancer surgery.

In individuals with spinal cord injury (SCI), rapid skeletal muscle atrophy and metabolic dysfunction pose profound rehabilitation challenges, often resulting in substantial loss of muscle mass and function. This study evaluates the effect of combined neuromuscular electrical stimulation (Comb-NMES) on skeletal muscle cross-sectional area (CSA) and inflammatory signaling within the acute phase of SCI. We applied a novel Comb-NMES regimen, integrating both high-frequency resistance and low-frequency aerobic protocols on the vastus lateralis muscle, to participants early post-SCI. Muscle biopsies were analyzed for CSA and inflammatory markers pre-and post-intervention. The results revealed a preservation of muscle CSA in the Comb-NMES group compared to a control group receiving transcutaneous electrical nerve stimulation and passive movements. Inflammatory signaling proteins such as TLR4 and Atrogin-1 were downregulated, whereas markers associated with muscle repair and growth were modulated beneficially in the Comb-NMES group. The study's findings suggest that early application of Comb-NMES post-SCI can attenuate inflammatory pathways linked to muscle atrophy and potentially promote muscle repair, advocating for its inclusion in early rehabilitation efforts. However, the small sample size and variability in injury characteristics emphasize the need for further research to corroborate these results across a more diverse and extensive SCI population.

Wafer-scale blue micro-light-emitting diode (micro-LED) arrays are fabricated with a pixel size of 12 μm, a pixel pitch of 15 μm, and a pixel density of 1692 pixels per inch, achieved by optimizing the properties of e-beam-deposited and sputter-deposited indium tin oxide (ITO). Although the sputter-deposited ITO (S-ITO) films exhibit a densely packed morphology and lower resistivity compared to those of the e-beam-deposited ITO (E-ITO) films, the forward voltage (VF) values of a micro-LED with the S-ITO films are higher than those with the E-ITO films. The VF values for a single pixel and for four pixels with E-ITO films are 2.82 V and 2.83 V, respectively, while the corresponding values for S-ITO films are 3.50 V and 3.52 V. This was attributed to ion bombardment damage and nitrogen vacancies in the p-GaN layer. Surprisingly, the VF variations of a single pixel and of four pixels with the optimized E-ITO spreading layer from five different regions are only 0.09 V and 0.10 V, respectively. This extremely uniform VF variation is suitable for realizing micro-LED displays to be used in AR and VR applications, circumventing the bottleneck in the development of long-lifespan and high-brightness organic LED devices for industrial mass production.

Several Lung diseases can result in lung damage structural changes, with lung transplantation as a only therapeutic option when the disease reaches an advanced progression, unfortunately, the success of lung transplantation is limited. In this context, lung bioengineering is considered a potential therapeutic alternative and the current approach is based on the use of the natural extracellular matrix (ECM) of the decellularized lung. The use of undifferentiated cells to seed an ECM can be a more practical strategy for lung bioengineering but the sterilization of the organ for this recellularization process is still a challenge. In this sense, Low-Level Laser Therapy (LLLT) can emerge as a potential treatment alternative. Therefore, the objective of this study was to verify if LLLT, can optimize lung recelularization with mesenchymal stem cells by cytokine modulation. For this, we decellularized lungs from C57BL/6 mice perfusing SDS 1% via pulmonary artery, sterilized with LLL (660nm, 100mW, 30s). Whole lung scaffolds were recellularized: (1) Recellularized Lung (2) Recellularized Lung + LLLT. Mesenchymal stem cells derived from human tooth pulp (DPSc) were seeded into lungs and incubated for 24 hours at 37ºC, 5% CO₂, in a bioreactor-like conditions, maintaining CPAP at 20cmH2O, 90% O2, and LLL at wavelength of 808nm, 100mW, 30s. Culture medium were analyzed at 24 hours. H&E, immunostaining, SEM and ELISA assay were performed. We produced viable biological scaffolds absent of DNA content, preserving glycosaminoglycans and collagen I, III and IV, fibronectin, laminin and elastin and lung structure (SEM). The levels of IL-6 and IL-8 were stable during the 24 hours of culture. IFN-γ levels showed significant difference in the DPSc and DPSc+LLLT group in the 24-hour evaluation. The levels of IL-10 decreased after LLLT. These findings highlight LLLT's role in cytokine regulation and contamination prevention during lung recellularization with mesenchymal stem cells. Overall, these results demonstrated that LLLT's modulated cytokine levels and could prevent contamination during the lung recellularization process with mesenchymal stem cells, offering promise for enhanced therapeutic strategies in lung bioengineering.

Background: Chronic lateral epicondylitis challenges the therapeutical approach; underlying mechanisms are incompletely understood; neuropathic pain and central and peripheral sensitization may explain the fact that botulinum toxin was found to play a role in pain and function management. Methods: We searched the literature for MeSH terms: lateral epicondylitis or synonyms and botulinum toxin. Results: We found 14 papers containing trials on botulinum toxin injection into the tendon or into the extensor muscles (particularly extensor carpi radialis brevis and extensor communis digitorum). We followed the administration pathways, doses, timing and side effects. Conclusion: With chronic course, the focus of the therapy shifts from the afflicted tendon to the inserting muscles, as muscle contracture may create a vicious loop to perpetuate and aggravate the disease. Doses, timing and side effects are discussed.

The development of one technology can be portrayed by common methods like Gartner’s hype cycle or S-curve, however, there is no method to characterize the feature distributions of multiple technologies within a specific domain. This study proposes a big data-based method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or causing potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach via comparing historical trends with literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.

A 68-year-old man with malignant metastatic melanoma in complete remission after Pembrolizumab treatment, developed 10 months after Pembrolizumab cessation due a grade 3 diarrhea, confusion, altered mental status, progressive memory loss, gait disturbance. He was admitted to the neurologic department and comprehensive neurological workup, brain magnetic resonance imaging, cerebral fluid analysis, EEG, blood test allowed the diagnosis of autoimmune encephalitis. The patient was treated with plasmapheresis, high dose of intravenous steroids and intravenous immunoglobulins. The patient improved and he is now well with a performance status 1. This case is interesting since the AE developed approximately 10 months after the cessation of immunotherapy, the underlying cancer was in complete remission and the AE showed a good response to the treatment done.

Previous studies reported a reduction of the perceived effort and an improvement of the perfor-mances of healthy subjects and patients when a motor task was combined with artistic images with respect to non-artistic images. This phenomenon, called Michelangelo effect, could contribute to the efficacy of art therapy in neurorehabilitation. In this study, the possible occurrence of this effect was tested in a cognitive task by asking to 15 healthy subjects and 17 patients with stroke to solve a digital version of the classical memory card game. Three different types of images were used in a randomized order: French cards, artistic portraits, and photos of famous people (to compensate the possible effects of face recognition). Healthy subjects were involved to test the usability and the load demanding of the developed system, reporting no statistically significant differences among the three sessions (p > 0.05). Conversely, patients had a better performance in terms of time (p = 0.014) and number of trials (p = 0.007) needed to complete the task in presence of artistic stimuli, accompanied by a reduction of the perceived effort (p = 0.033). Furthermore, artistic stimuli, with respect to the other two types of images, seemed more associated to visuo-spatial control than to linguistic functions.

Ice formation on aircraft surfaces poses significant safety risks, and current detection systems often struggle to provide accurate, real-time predictions. This paper presents the development and comprehensive evaluation of a smart ice control system using a suite of machine learning models. The system utilizes various sensors to detect temperature anomalies and signal potential ice formation. We trained and tested supervised learning models (Logistic Regression, Support Vector Machine, Random Forest), unsupervised learning models (K-means clustering), and neural networks (Multi-Layer Perceptron) to predict and identify ice formation patterns. Experimental results demonstrate that our smart system, driven by machine learning, accurately predicts ice formation in real-time, optimizes de-icing processes, and enhances safety while reducing power consumption. This solution holds the potential for improving ice detection accuracy in aviation and other critical industries requiring robust predictive maintenance.

The supply chain has incorporated products by putting them into hair scarfs. This study introduces the use of mini chips in health and beauty products and can reduce fatigue through enhanced sleep patterns. The mini chip could be placed in the scarf and used as a prototype. RFID technology provides the supply chain with specific information that is used to identify products and make communication easier. (Muhammad, et. al. 2013) This paper presents a new tool herein referred to as a scarf prototype which is developed to analyse EMG (electromyogram), ECG (electrocardiography), EEG (electroencephalogram), and EOG (electro-oculogram) signals that focuses in the area of sleep disorders. The mini chips used can be used to determine a solution for sleep disruption by using automated analytics. This could lead to improvement in our understanding of sleep disruption and overall sleep physiology. Automated technology allows repeated measurements, evaluation of sleep patterns, and provide suggestions to improve a person’s quality of sleep. This analysis compares the use of polysomnography and the scarf prototype. The analytics provide models and shows correlation between variables, such as EMG, ECG, EEG, and EOG. This study shows that the results from the scarf prototype is just as reliable as the original method, polysomnography.

Firefighters Protective Clothing consist of several layers of different properties and functions. The layers create barriers against dangerous outer factor. However, they are also barriers to transport of sweat produced by human organism. It causes that very often sweat in the form of vapor is not transferred effectively from the human organism through the FPC to environment. This leads to sweat condensation on the skin of the FPC user causing his discomfort. In order to ensure as good as possible physiological comfort of firefighter while firefighting and other rescue actions it is necessary to take into consideration the transport of condensed sweat in the multilayer textile set creating the FPC. In the presented work four variants of multilayer textile sets for the FPC have been tested in the aspect of liquid moisture transport. Measurement was performed by means of the Moisture Management Tester (MMT290) – innovative instrument manufactured by the SDL Atlas. Obtained results showed that the transfer of sweat in liquid form occurs only in the inner, next to skin layer of the multilayer sets for the FPC.

Background: Positive person-centered attributes of inpatient rehabilitation need to be identified from the patient perspective to be further developed and sustained. Purpose: To identify which attributes patients openly evoke as being great care experiences, using an open appreciative inquiry during the inpatient rehabilitation stay. Methods: Qualitative secondary analysis of appreciative patient comments during a bedside patient experience rounding, facilitated by a neutral party. Two independent analysts employed an inductive, summative form of content analysis. Results: Among 150 patients rounded, 122 provided categorizable appreciative accounts. Over two thirds of the patients (67.2%) focused on “staff attributes” in their great-experience accounts. Those attributes were mostly interpersonal such as being “attentive & caring - beyond clinical duty” and being “encouraging (but not too hard) & reassuring”. These interpersonal staff attributes were reported with words showing deep levels of personal significance or patient appreciation. Beyond staff attributes, the perceived quality of the “patient care” (31.1%) and opportunities for “leisure and social activities” (9.0%) were also frequently evoked. Amenities like food or costumer service were the least evoked, rarely so as an exclusive attribute (0.8% for each). Conclusions: The human(e) factor, especially the interpersonal qualities of staff, emerged as greatly appreciated from the patient experience perspective during inpatient rehabilitation. These experiences help identify which person-centered attributes of care might be further developed and sustained.

In the drive towards environmental restoration and sustainable energy solutions, effective utilization and management of agricultural crop residues are vital to mitigate greenhouse gas (GHG) emissions and waste accumulation. Converting biogenic wastes into biofuels and bioproducts has the potential to tackle the energy crisis and contribute to environmental remediation. In this investigation, teff husk, primarily generated in Ethiopia during teff production within the agro-industrial sector, is used as the feedstock for the slow pyrolysis process. In Ethiopia, it is estimated that over 1.75 million tons of teff husk are generated annually, with a significant portion being incinerated in the fields, resulting in substantial environmental pollution. This study focuses on assessing teff husk as a potential material for slow pyrolysis, a crucial stage in biochar production, to tap into its biochar-producing potential. To determine the composition of the biomass, the teff-husk was initially analyzed by thermogravimetry. The significant presence of fixed carbon indicates that teff husk is a viable candidate for pyrolytic conversion into biochar particles. The process of slow pyrolysis took place at three varied temperatures—specifically, at 400, 450, and 500 °C. Process parameters of slow pyrolysis such as reaction time, temperature, and heating rate were enhanced to obtain maximum biochar yields. The highest biochar yield of 43.4 wt% was recorded at optimized reaction time, temperature, and heating rate of 120 min, 400 °C, and 4.2 °C/min, respectively. Furthermore, biochar's physicochemical, SEM-EDX, FTIR, and TGA characterization were performed. The thermal stability and carbon content of biochar were found to rise at higher temperatures. Unlike fuel recovery, the results suggest that teff-husk can be used as a feedstock for biochar production.

Populational aging is marked by chronic non-communicable diseases, such as metabolic syndrome (MetS). IL-10 and IL-1β are pleiotropic cytokines with multiple biological effects linked to metabolic disorders. This cross-sectional study assessed 193 participants' IL-10 and IL-1β serum levels regarding their role in developing MetS, clinical characteristics, and their IL1B rs1143627 and IL10 rs1800890 variants' genotype frequencies in a population over 60. IL-10 levels correlated weakly with HDL levels and fat mass and inversely with triglycerides, glucose, glycated hemoglobin, and estimated average blood glucose levels. IL-10 levels were also indirectly influenced by the patient's T2DM duration, lean mass amount, and bone mineral content. Participants with altered HDL, elevated serum glucose, raised HbA1c levels, or those over 80 had reduced serum IL-10 levels compared to those with normal levels or other age groups, respectively. Women also had higher serum IL-10 levels than men. Dissimilarity, IL-1β levels correlated directly only with the number of total leukocytes and segmented neutrophils, showing only significant variations with self-reported alcohol consumption. Our study also found that those with the IL10 AA genotype (lower IL-10 levels) had a significantly higher risk of developing MetS. These findings may help direct future research and more targeted therapeutic approaches in older adults.

Being able to express broad families of equivariant or invariant attributed graph functions is a popular measuring stick of whether graph neural networks should be employed in practical applications. However, it is equally important to learn deep local minima of losses (i.e., with much smaller loss values than other minima), even when architectures cannot express global minima. In this work we introduce the architectural property of attracting GNN optimization trajectories to local minima as a means of achieving smaller losses. We take first steps in satisfying this property for losses defined over attributed undirected unweighted graphs with a novel architecture, called Universal Local Attractor (ULA). The latter refines each dimension of end-to-end trained node feature embeddings based on graph structure to track the optimization trajectories of losses satisfying some mild conditions. The refined dimensions are then linearly pooled to create predictions. We experiment on 10 tasks, from node classification to clique detection, on which ULA is comparable with or significantly outperforms popular alternatives of similar or greater theoretical expressive power.

The mHealth is a term that is used for mobile health supported by smart devices such as mobile phones, tablets, and wearable smart devices, etc. The smart devices strengthen the efficiency and effectiveness of interaction with patients, physicians, and specialists. Patients nowadays would like to be intimately involved in their diagnosis as well as to make more informed decisions concerning their care. It has begun to measure the success of the quality of treatment. This was a reason that patients trust mHealth to provide them with consistency in their communications with the physicians. Most wireless strategies do not measure up to this standard so that patient engagement ultimately ended up decreasing. The blockchain can boost mHealth through storing and sharing electronic data securely and transparently. It can enhance the accessibility of patient information in real-time. The Internet of Things (IoT) provides a unique identification number to every connected device such as mobile devices, medical devices, and wearable devices. This framework uses the blockchain and IoT technologies together to provide quick help to the patients, monitor remotely, reduce the cost and unnecessarily hospitalization physically and find the real diagnosis. In order to increase patient involvement, mHealth framework with blockchain and IoT technologies has built with the key objective of providing patients with full information on their treatment and diagnosis.

In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via Stereolithography (SLA) of features at the micro-scale. Using a custom benchmark part, deviations in feature dimensions (micro-pores diameters and depths) were detected and measured by confocal microscopy. Samples characterization and experimental observations allowed the identification of inaccuracy sources, mainly due to the laser beam scanning strategy and to the incomplete removal of uncured liquid resin in post-processing (i.e., IPA washing). As a technology baseline, the measured dimensional errors on pores diameters were up to -46%. A compensation method of the nominal laser spot diameter (85m) was defined and implemented resulting in relevant improvements in dimensional accuracy of 9.8% and 11.3% on full-open pores diameter and depth, respectively. Further investigation was performed on a customized test part to adjust the calibration procedure. The measurements revealed that the estimate of the laser beam spot size is 968m. Measurements on micro-pores having different sizes revealed that a constant compensation parameter (i.e., C=85, 96, 120m) is not fully effective at the micro-scale, where average errors still remain at -24%, -18.8%, and -16% for compensations equals to 85, 96 and 120m, respectively. A further experimental campaign allowed the identification of an effective nonlinear compensation law where the compensation parameter depends on the micro-feature size C= f (D). Results show a sharp improvement in dimensional accuracy on micro-pore fabrication, with errors consistently below +8.2%. The proposed compensation method can be extended for the fabrication of any micro-features without restrictions on the specific technology implementation. Concerning the inaccuracy introduced by the laser path, experimentation with a customized part allowed the optimization of the part orientation to reduce the errors and their differences along the X and Y machine axes.

This paper describes a system that is used to gather data from Smart Home sensors measuring energy consumption, analyze and predict future consumption by using machine learning algorithm for prediction. It focuses on the architecture of the system as well as the need for such system to exist in practice – why the consumption should be measured at all and how that measurement can be put to good use for achieving energy efficiency.

The domain of construction engineering and management is evolving rapidly. Optimizing resource positioning is crucial for meeting the challenges of efficiency and productivity. This research presents a novel methodology that incorporates Agent-Based Modeling (ABM), Building Information Modeling (BIM), and Bluetooth Low Energy (BLE) technologies, augmented with a Client Early Warning System (CEWS) and a Decision Support System (DSS). What makes this study unique is that it provides a real-time methodology for resource allocation and project management, tailored to address the complexities of modern construction projects. The application of this integrated technological framework marks a significant leap in construction management, empowering data-driven decision-making and significantly improving project efficiency. Empirically validated through a case study at Helwan University's College of Engineering and substantiated by expert surveys, this research showcases a transformative model for resource management. The findings not only underscore the practical applicability of this approach but also open new horizons for its adoption in global construction practices, offering insights for future research endeavors focused on expanding these methodologies to a wider range of resources.

Background/Objectives: The aim of this study was to investigate feasibility and safety of neuromuscular electrical stimulation (NMES) in patients on extracorporeal membrane oxygenation (ECMO) and assess thoroughly any potential adverse events. Methods: We conducted a safety and feasibility study including 16 ICU patients on ECMO support who were admitted to the cardiac surgery ICU from January 2022 to December 2023. Patients underwent a 45-minute NMES session, still on ECMO support, that included a warm up phase of 5 minutes, a main phase of 35 minutes and a recovery phase of 5 minutes. NMES was implemented on vastus lateralis, vastus medialis, gastrocnemius and peroneus longus muscle of both lower extremities. Two stimulators delivered biphasic, symmetric impulses of 75 Hz, with 400 μsec pulse duration, 5 sec on (1,6 sec ramp up and 0,8 sec ramp down) and 21 sec off. The intensity levels aimed to cause visible contractions and be well tolerated. Primary outcomes of the study were feasibility and safety, evaluated by whether the NMES sessions were successfully achieved, and by any adverse events and complications. Secondary outcomes included indices of rhabdomyolysis from biochemical blood test, 24 hours after the application of NMES. Results: All patients successfully completed their 45-minute NMES session, without presenting hemodynamic deterioration, adverse events or signs of rhabdomyolysis. The majority of patients achieved type 4 and 5 quality of muscle contraction. Conclusions: NMES is a safe and feasible exercise methodology in patients supported with ECMO.

Methylation is a biochemical process involving the addition of a methyl group (-CH3) to various chemical compounds. It plays a crucial role in maintaining the homeostasis of the endothelium, which lines the interior surface of blood vessels and has been linked, among other conditions, to coronary artery disease (CAD). Despite significant progress in CAD diagnosis and treatment, intensive research continues into genotypic and phenotypic CAD biomarkers. This review explores the significance of the methylation pathway and folate metabolism in CAD pathogenesis, with a focus on endothelial dysfunction resulting from deficiency in the active form of folate (5-MTHF). We discuss emerging areas of research into CAD biomarkers and factors influencing the methylation process. By highlighting genetically determined methylation disorders, particularly the MTHFR polymorphism, we propose the potential use of the active form of folic acid (5-MTHF) as a novel CAD biomarker and personalized pharmaceutical for selected patient groups. Our aim is to improve the identification of individuals at high risk of CAD and enhance their prognosis.

Stress urinary incontinence (SUI) influences the quality of life of women. The research applied ICIQ LUTS qol (The International Consultation Incontinence Questionnaire Lower Urinary Tract Symptoms quality of life) as a tool to measure the quality of life (QOL) of patients with stage 1 SUI in many areas. 140 perimenopausal women who participated in a urodynamic test at Department of Gynecology, Endocrinology and Gynecologic Oncology, Pomeranian Medical University, Police, Poland in the years 2013-2015 were enrolled in the study. Patients were divided into two groups: A and B. Two questionnaires were applied: the author’s original questionnaire and the standardized ICIQ LUTS qol. Gymnastic plans were recommended 4 times per week for the period of 3 months. The plan for group A included the exercises of pelvic floor muscles (PFM) with the simultaneous tension of the transverse abdominal muscle (TrA) and for group B included the exercises of PMF without TrA. The evaluation of the quality of life of patients with stage 1 SUI with the use of ICIQ LUTS qol among patients from groups A and B after therapy showed a significant improvement in most areas. More impact is observed in the physiotherapy of both PFM with TrA.

The number of physical devices connected to the Internet such as vehicles, household appliances and other "things" has been steadily increasing in recent years, thus forming the basis for the Internet of Things (IoT). IoT ecosystem extends beyond country borders and application domains, combining thousands of versatile devices that differ in terms of their structures, capabilities, and available resources. It is therefore not surprising that the landscape of wireless communication technologies and the degree of IoT devices available today is excessively broad and diverse. Interference between networks leads to frame collisions and consequent packet loss. Frame collisions occur when two or more packets overlap in time and frequency and use the same LoRa parameters, i.e. the same spreading factor (SF), bandwidth (BW) and carrier frequency (CF). When most devices use the same configuration, collision probability is higher. The probability of frame collisions is also affected by traffic characteristics, particularly the periodicity of the transmission and the payload size. Larger payload sizes and more frequent transmissions accumulate with higher time on air (ToA) and channel occupancy [1]. Transmission powers and the location of the gateways also influences this situation. The aim of this experimental work is to verify the effects of collisions and interference in LoRaWAN, regarding the periodicity of transmissions and the payload sizes. The hardware used is an Arduino board programmed in C/C++, a LoRa Bee module operating at a frequency of 868 MHz and a LoRaWAN LG02 gateway. Different scenarios were tested in an outdoor environment, with Line of Sight and Non-Line of Sight, and different variables such as: SFs equal to 7, 9 and 12; distances of 20, 40 and 60 meters and different data sizes of 14, 32 and 51 bytes, over the same channel. The packets with different SF’s and different data sizes were transmitted in a (pseudo) random way, one after the other, overlapping different SFs in the same channel and testing this way (theoretical) orthogonality among them. By fixing the bandwidth in 125 kHz, and testing different SFs we intend to increase the probability of cause packet loss by applying to our connection a combination of emitted power/short distance that leaves an interference power received, strong enough [2] for the effect. We validate our conclusions by analyzing ToA according with RSSI and different data sizes, the RSSI for different SFs and different data sizes and finally the packet loss through the PDR-Packet Delivery Ratio for a better understanding of the reliability of packet transmission in our link.

Tissue-like phantoms are widely used as a model for mimicking the optical properties of live tissue. This paper presents the results of a diffusion reflection method as well as fluorescence lifetime imaging microscopy measurements of fluorescein-conjugated gold nanorods in solution as well as inserted in solid tissue-imitating phantoms. A lack of consistency between the fluorescence lifetime results of the solutions and the phantoms raises a question about the ability of tissue-like phantoms to maintain the optical properties of inserted contrast agents.

Curcumin, a polyphenol with a rich history spanning two centuries, has emerged as a promising therapeutic agent targeting multiple signaling pathways and exhibiting cellular-level activities that contribute to its diverse health benefits. Extensive preclinical and clinical studies have demonstrated its ability to enhance the therapeutic potential of various bioactive compounds. While its reported therapeutic advantages are manifold, predominantly attributed to its antioxidant and anti-inflammatory properties, its efficacy is hindered by poor bioavailability stemming from inadequate absorption, rapid metabolism, and elimination. To address this challenge, nano-delivery systems have emerged as a promising approach, offering enhanced solubility, biocompatibility, and therapeutic effects for curcumin. We have analyzed the knowledge on curcumin nano-encapsulation and its synergistic effects with other compounds, extracted from electronic databases. We discuss the pharmacokinetic profile of curcumin, current advancements in nano-encapsulation techniques, and the combined effects of curcumin with other agents across various disorders. By unifying existing knowledge, this analysis intends to provide insights into the potential of nano-encapsulation technologies to overcome constraints associated with curcumin treatments, emphasizing the importance of combinatorial approaches in improving therapeutic efficacy. Finally, this compilation of study data aims to inform and inspire future research into encapsulating drugs with poor pharmacokinetic characteristics and investigating innovative drug combinations to improve bioavailability and therapeutic outcomes.

Recent discoveries of potential ice particles and ice-cemented regolith on celestial bodies like the Moon and Mars have ushered in new opportunities for developing technologies to extract water, facilitating future space missions and activities on these celestial surfaces. This study delves into the potential for water extraction from regolith through an experiment designed to test water recuperation from regolith simulant under varying gravitational conditions. The resultant water vapor extracted from the regolith is re-condensed on a substrate surface and collected in liquid form. Three types of substrates, hydrophobic, hydrophilic, and grooved, are explored. The system’s functionality was assessed during a parabolic flight campaign simulating three distinct gravity levels: microgravity, lunar gravity, and Martian gravity. Our findings reveal that the hydrophobic surface demonstrates the highest efficiency due to drop-wise condensation, and lower gravity levels result in increased water condensation on the substrates. This experiment aims to provide insights into in-situ water recovery, which is pivotal for establishing economically-sustainable water supplies for future missions.

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of dual-phase-lag heat conduction model with the Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag-Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

Objective. To test the hypothesis that a session of unilateral tSMS over the dominant primary motor cortex (M1) in healthy subjects produces a transient decrease in manual dexterity. Methods. A triple-blinded randomized control trial was conducted in 40 healthy participants who were allocated to real- or sham-tSMS group. Each participant underwent one session of tSMS and were assessed before, immediately after, 15 minutes, and 40 minutes after stimulation. Outcome measures were Finger Tapping Test (FTT), 5-seconds FTT, nine Hole Peg Test, and the hand grip assessment. Results: Significant differences were found between groups 15 minutes after stimulation in favor of the tSMS group in the FTT (2.2 ± 0.99; CI= .18-4.22; d= .84p=.034), and 5-second FTT (4.2 ± 1.98; CI= .199-8.2, d= .68; p=.04). In the intragroup analysis, a significantly lower number of taps 15 minutes after stimulation was observed in sham group (3.3 ± 1.01; CI= .47-6.13; d= .42; p=.015), but not in the real stimulation group. Conclusion: A unilateral session of tSMS in the M1 of the dominant hemisphere of healthy individuals does not reduce manual dexterity or grip strength. This technique could have benefits on the motor performance of the arm contralateral to the stimulated motor cortex

Urban tourism has witnessed significant growth in recent years, driven by various factors that foster the transformation of urban areas into tourist hotspots. These factors include a rising interest in culture and heritage, ongoing urban redevelopment and conversion processes, and the introduction of new experiences and activities. Two particularly influential factors contributing to this growth are low-cost travel, which facilitates more accessible access to urban destinations, and the emergence of new types of accommodation, such as short-term rental (also known as local accommodation or Airbnb-type accommodation). This sudden and continuous growth of this type of tourist accommodation brings new challenges to cities and local communities. On the positive side, it contributes to job opportunities, the dynamism of the local economy, and urban revitalization. On the other hand, it also represents more difficulty in retaining the local population in city centers, as more and more buildings are converted into hostels and tourist apartments. In this context, it becomes essential to conduct studies evaluating the impacts of these dynamics in urban areas and how they influence residents' and visitors' perspectives. Therefore, this study aims to identify the main transformations resulting from short-term rental (STR) development, focusing on the impacts felt by tourists and residents in Porto. A questionnaire was distributed to tourists and residents in Porto's city center in June 2023. The results reveal differences in opinions between tourists and residents, particularly concerning historical heritage preservation, local population relocation, and reduced housing availability for residents. Nevertheless, there is a similarity in their opinions regarding the stimulus that local accommodation brings to the economy and the rehabilitation of buildings and urban areas. This study reinforces the need to pay more attention to the future development of tourist accommodation in city centers, especially regarding local policies and territorial planning, to balance better tourism development and local communities' quality of life.

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSD) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support to the prevention of back pain or the development of MSD. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, Skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal, valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures and cognitive load detection from physiological signals.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

The physiological characteristics of liquid digestate retrieved from various biogas plants based in northern Greece are presented. Preliminary photocatalysis experiments on inoculated liquid di-gestate sampled showed that disinfection is possible when pre-treated digestate with a combination of centrifuge-flocculation-μFiltration after 5.5 hours of 0.7g/L suspended TiO2 under UVA illumination for the experimental conditions is used. A novel design photocatalytic nanofiltration reactor was implemented for disinfection experiments on pre-treated liquid digestate, giving promising results. This work sets the basis for the efficient operation and engineering application of technology collaboration with photocatalysis as the final step for liquid digestate sanitation and reusable water recovery.

This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.

The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.

Cigarette smoke (CS) is a major driver of many respiratory diseases including chronic obstructive pulmonary disease (COPD) and non-small cell lung cancer (NSCLC). Tobacco causes oxidative stress, impaired phagocytosis of alveolar macrophages (AMs), and alterations in gene expression in the lungs of smokers. MicroRNAs (miRNAs) are small non-coding RNAs that influence several bi-ological processes and interfere with several regulatory pathways. The purpose of this study was to assess the effect of active CS on miRNAs expression in AMs obtained from bronchoalveolar lavage (BAL) of ever- or never-smoker subjects and patients with COPD or NSCLC. BAL specimens were collected from 43 sex-matched subjects to determine the expression of has-miR-34a-5p, 17-5p, 16-5p, 106a-5p, 223-5p, and 20a-5p before and after in vitro CS exposure by RT-PCR. In addition, bioin-formatic analysis of miRNAs target genes linked to inflammation was performed. Distinct and common miRNA expression profiles were identified in response to CS, suggesting their possible role in smoking-related diseases. It is noteworthy that, following exposure to CS, the expression levels of hsa-miR-34a-5p and 17-5p in both ever- or never-smokers, 106a-5p in never-smokers and 20a-5p in ever-smokers, shifted towards those found in individuals with COPD, suggesting them like a risk factor in developing this lung condition. Moreover, we identified miRNA targets involved in the immune system or AMs property regulation using in silico analysis. In conclusion, our study iden-tified miRNA signatures in AMs exposed to CS, indicating that CS is an important driver of epige-netic changes that contribute to the onset of various lung diseases.

Three-dimensional (3D) printing technology, also known as additive manufacturing (AM), has emerged as an attractive state-of-the-art tool for precisely fabricating functional materials with complex geometries, championing several advancements in tissue engineering, regenerative medicine, and therapeutics. However, this technology has an untapped potential for biotechnological applications, such as sensor and biosensor development. By exploring these avenues, the scope of 3D printing technology can be expanded and pave the way for groundbreaking innovations in the biotechnology field. Indeed, new printing materials and printers would offer new possibilities for seamlessly incorporating biological functionalities within the growing 3D scaffolds. Herein, we review the extrusion-based 3D printing modalities emphasizing biosensor applications. We highlighted the application of natural, synthetic, and composite biomaterials as 3D-printed soft hydrogels. Emphasis was placed on the approach by which the sensing molecules were introduced during the fabrication process. Finally, the future perspective was provided.

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. Prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart-sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart-sensing chairs, with a specific focus on the strategies used for posture detection and classification, as well as the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, and Google Scholar databases yielded 34 pertinent studies that utilize non-invasive methods for posture monitoring. The analysis reveals that Force Sensing Resistors (FSR) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy, due to constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.

Globally, there is massive uptake and explosion of data and challenge is to address issues like scale, pace, velocity, variety, volume and complexity of this big data. Considering the recent epidemic in China, modeling of COVID-19 epidemic for cumulative number of infected cases using data available in early phase was big challenge. Being COVID-19 pandemic during very short time span, it is very important to analyze the trend of these spread and infected cases. This chapter presents medical perspective of COVID-19 towards epidemiological triad and the study of state-of-the-art. The main aim this chapter is to present different predictive analytics techniques available for trend analysis, different models and algorithms and their comparison. Finally, this chapter concludes with the prediction of COVID-19 using Prophet algorithm indicating more faster spread in short term. These predictions will be useful to government and healthcare communities to initiate appropriate measures to control this outbreak in time.

External motivational stimuli have been shown to improve athletic performance. However, the neurophysiological mechanisms underlying this improvement remain poorly understood. This randomized crossover study investigated the effects of music and verbal encouragement on measures of muscle excitation and myoelectric manifestations of fatigue in the biceps brachii (BB) and brachioradialis (BR) muscles during an endurance task. Fifteen untrained (mean age 29.57 ± 2.77 years) and 13 trained individuals (mean age 32.92 ± 2.90 years) were included. The endurance task, performed to exhaustion, consisted of keeping the dominant arm flexed to 90 degrees while holding a dumbbell loaded to 80% of 1RM with a supine grip in three randomized conditions: standard, with self-selected music, and with verbal encouragement. The untrained subjects showed an increase in task duration of 15.26% (p < 0.003) with music and 15.85% (p < 0.002) with verbal encouragement compared to the condition without external stimuli. There were no significant differences in the myoelectric manifestations of fatigue between the dif-ferent conditions. Regarding the muscle excitation metrics, although the mean amplitude, peak value and area under the curve remained unchanged across conditions, a significant reduction in the trend coefficient, indicating motor unit recruitment over time, was observed with both music (BB: -10.39%, p < 0.001; BR: -9.40%, p < 0.001) and verbal encouragement (BB: -7.61%, p < 0.001; BR: -6.51%, p < 0.001) compared to the standard condition. For the trained participants, no significant differences were observed between conditions in terms of task duration and outcome measures related to muscle excitation and myoelectric manifestations of fatigue, suggesting the possible presence of a ceiling effect on motivation. These results highlight the significant role of external motivational stimuli, such as music and verbal encouragement, in improving task performance in untrained subjects, probably through more ef-fective and efficient recruitment of motor units.

Due to the high specific surface area of titanium aluminide powders, significant and unavoidable surface oxidation takes place during processing. The resulting oxides disrupt the conventional powder metallurgical process route (pressing and sintering) by reducing green strength and sintered properties. Oxide-free particle surfaces offer the potential to significantly increase particle bond strength and enable the processing of difficult-to-press material powders. In this work, the effect of milling titanium aluminide powder in a silane-doped atmosphere on the component properties after pressing and subsequent sintering was investigated. Ball milling was used to break up the oxide layers and create bare metal surfaces on the particles. With the help of silane-doped inert gas, the oxygen partial pressure was greatly reduced during machining. It was investigated whether oxide-free surfaces could be produced and maintained by milling in silane-doped at-mospheres. Furthermore, the resulting material properties after pressing and sintering were an-alyzed using density measurements, hardness tests, EDX-measurements and micrographs. It was concluded, that ball milling in a silane-doped atmosphere produces and maintains oxide-free particle surfaces. These oxide-free surfaces and smaller particle sizes improve the component properties after pressing and sintering.

In the context of improving aircraft safety, this study focuses on the development and evaluation of a graphene-based ice detection system using an environmental chamber. The research is driven by the need for more accurate and efficient ice detection methods, crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors’ heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors, tested under varied and controlled atmospheric conditions, exhibit a remarkable potential in enhancing ice detection systems for aircraft. Their lightweight, efficient and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

Background: The prevalence of falls among the older population underscores the imperative of comprehending human adaptations to gait perturbations. Dual-belt treadmills offer a controlled setting for such investigations. The purpose of this study was to examine the effect of acceleration of one belt of the treadmill during three different phases of the gait cycle on kinematic and kinetic parameters and relate these changes to unperturbed gait. Methods: Twenty-one healthy young females walked on a treadmill in a virtual environment, in which five unexpected perturbations were applied to the left belt at the Initial Contact (IC), Mid Stance (MS), and Pre-Swing (PS) phase of the gait cycle. Data from the undisturbed gait and the first disturbance of each trial were extracted for analysis. Results: All perturbations significantly affected the gait pattern, mainly by decreasing the knee extension angle. The perturbation in the IC phase had the most significant effect, resulting in a 248.48% increase in knee flexion torque. The perturbation in the MS phase mainly affected plantar flexion torque, increasing it by 118.18%, while perturbation in the PS phase primarily increased the hip extension torque by 73.02%. Conclusions: The presence of perturbations in the IC and PC phases caused the most aggressive and significant changes in gait parameters.

Heavy metals are dangerous contaminants constituting a threat to human health because they persist in soils and are easily transferred into the food chain, causing damages to human health. Among heavy metals, nickel appears to be one of the most dangerous responsible for different disorders. Public health protection requires nickel detection in environment and food chains; a simple, rapid and sensitive method to detect nickel contamination is represented by biosensors. In this paper we report the setup of a whole-cell based system, in which protoplasts, obtained from Nicotiana tabacum leaves, were used as transducers to detect the presence of heavy metal ions and, in particular, nickel ions. Protoplasts were genetically modified with a plasmid containing the GFP reporter gene under the promoter region of a sunflower small HSP gene control. By this device the presence of heavy metal ions was detected. Thus, the possibility to use this whole-cell system as a novel tool to detect the presence of nickel ions in food matrices was assessed.

(1) Background: Breast cancer-associated lymphedema (BCRL) is a major complication of breast cancer treatment, affects the quality of life of breast cancer survivors who develop it. Manual lymphatic drainage has a beneficial result in combating lymphedema in patients who have undergone breast cancer surgery. The hypothesis consists in complete decongestive therapy reduces the lymphedema at the level of the upper limb on the side of mastectomy. (2) Methods: Twenty-five patients with radical mastectomy and upper limb lymphedema on the mastectomy side were included in this study. Upper limb circumferences were measured with a tape measure at the following levels: metacarpophalangeal joint; distal radioulnar joint; 10 cm distal to lateral epicondyle; 5 cm proximal to elbow joint; 10 cm proximal to lateral epicondyle. (3) Results: Patients received twice weekly manual lymphatic drainage, multi-layer compression bandaging and upper limb muscle stimulation exercises on the mastectomy side twice weekly. The circumference assessed at the level of the metacarpophalangeal joints and distal radio-carpal joint decreased at the final evaluation. Forearm perimetery - 5 and 10 cm distal to the lateral epicondyle the circumference decreased at the final assessment. (4) Conclusions: Patients with breast cancer-related lymphedema should receive comprehensive decongestive therapy in the early stage of lymphedema to achieve rapid clinical results and improved quality of life

Plastic contamination of small dimensions (

Stroke causes disability and significantly affects patient quality of life. Post-stroke rehabilitation of upper limb function is crucial, as it affects daily activities and individual autonomy. Traditional rehabilitation methods often require frequent visits to specialized centers, which can be costly and challenging. This study investigated the effectiveness of a home-based self-training device, "Kenko Yusuri," in improving upper limb function in patients with chronic stroke. This multicenter prospective intervention study used a quasi-randomized controlled trial design. Ninety-three outpatients from five hospitals in Japan were enrolled and assigned to either the intervention group who used the "Kenko Yusuri" device or the control group who underwent conventional rehabilitation. All patients received botulinum toxin type A (BoNT-A) injections to treat upper limb spasticity. The primary outcome measure was the Fugl-Meyer Assessment (FMA) of upper-extremity motor function. Secondary outcomes included the modified Ashworth scale (MAS) score, range of motion (ROM), and chronic pain assessments. The intervention group demonstrated significantly greater improvements in FMA total scores than the control group. Patients with moderate paralysis experienced the greatest benefits from the intervention. There were no significant between-group differences in MAS scores or ROM measurements. Pain and wrist ROM showed improvement in the intervention group. Home-based training with a shaking device significantly improved upper limb function in patients with moderate chronic post-stroke paralysis. This approach aligns with the principles of use-dependent plasticity and offers a feasible and cost-effective alternative to traditional rehabilitation methods.

Kinesiophobia (fear of movement) has been recognized as a significant barrier to recovery and rehabilitation in patients after total knee arthroplasty (TKA). Our goal was to identify predictors of kinesiophobia and examine its correlation with early functional outcomes in TKA recipients. On the first and fifth postoperative days (POD1 and POD5), we evaluated pain using the International Pain Outcomes Questionnaire (IPO-Q) and created multidimensional pain composite scores (PCS). Pain Composite Score-total (PCStotal) assesses the overall impact of pain, taking into account outcomes of pain intensity, pain-related interference with function, and emotions and side effects. Functional status on POD 5 was determined by the Barthel index, 6-minute walking test, and knee range of motion. Kinesiophobia was measured on POD5 using the Tampa Scale for Kinesiophobia (TSK) and multivariable logistic regression was used to assess factors associated with kinesiophobia. Among 75 TKA patients, 27% exhibited kinesiophobia. The final regression model highlighted PCStotal on POD5 (OR=6.2, CI=1.9-19.9), PCStotal (OR=2.1, CI=1.2-3.8) on POD1, and the intensity of chronic pain before surgery (OR=1.4, CI=1.1-2.1) as significant kinesiophobia predictors. On POD5, those with kinesiophobia showed increased dependency, slower gait, and poorer knee extension recovery. This study highlights the importance of identifying and addressing kinesiophobia in TKA patients to optimize functional outcomes and improve recovery. Also, it is important to assess not only pain intensity but also the physical and emotional interference caused by pain, which can contribute to the development of kinesiophobia.

With the increasing interest in CM as a potential source of nutrients and bioactive compounds, there arises a necessity to delve into methods for its processing and drying. Over recent decades, a significant amount of research has been conducted on the chemical composition of CM and its potential health benefits. Consequently, this study is directed towards examining CM drying technology, including an analysis of the influence of different drying methods on its chemical composition and nutri-tional properties. The objective of this review is to systematically summarize the available literature on CM drying processes and their impact on its chemical composition. Special attention is devoted to comparing various drying methods such as freeze-drying, spray drying, and freeze-drying, as well as investigating factors affecting the quality of dried CM. To achieve this goal, we scrutinize a series of studies published between 2003 and 2022 to identify optimal drying methods that promote the maximum preservation of CM's nutritional components and bioactive compounds. Additionally, we explore possibilities for reducing drying time to enhance production efficiency. This research is grounded in the analysis of data from diverse scientific research databases, including Scopus and Google Scholar, encompassing a systematic review of scientific literature and a comparative analysis of the obtained results.

Background: Sexual dysfunction is the most common side effect encountered by cervical cancer patients during and after treatment, and rehabilitation strategies are needed to improve their quality of life. A variety of rehabilitation strategies have been reported in previous studies, varying by country, race and even continent. However, the common rehabilitation strategies used by cervical cancer survivors in Tanzania and their outcomes are unknown. The study aimed to unfold sexual rehabilitation strategies and outcome practiced by cervical cancer survivors who experienced sexual dysfunction during and after treatment in North-western Tanzania. Methods: Descriptive qualitative study were employed. Phenomenology study design used with a sample of 7 participants and data corrected via in-depth interview guided by 11 questions. Audiotaped and transcribed to get main themes and sub themes through deductive analysis. Results: Most participants reported inability to achieve an orgasm and low libido and dyspareunia after treatment. Most participants were eagerly to have their sexual activity recovered. However, they were not familiar with any rehabilitation strategy after the experience of sexual dysfunction. Even though, some of the participants attempted various interventions, but all were unsatisfactory leading to a significant decline in sexual interest and activity. Conclusion: Cervical cancer survivors are not familiar with any sexual rehabilitation strategies. Health education concerning various sexual rehabilitation strategies should be provided to survivors during and even after treatment.

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

A common problem in education has always been to get students to achieve an adequate level of motivation and engagement. Many evidences show that gamification has the potential to make learning more engaging, motivating and effective. However, it is essential to design gamified experiences thoughtfully, ensuring that they align with educational objectives and promote meaningful learning outcomes, and so the lack of established design methodologies can lead to unintended consequences.

Drosophila melanogaster serves as a important research model both for genetic investigations and for the study of metal toxicity, facilitating the elucidation of physiological mechanisms comparable to those of human organisms. In our research, we evaluated the toxicity effect resulting from exposure to various concentrations of lead and copper on the prolificity rates throughout the life cycle (egg-adult) of four genotypes of Drosophila melanogaster: wild-type (control), white, brown, and white-vestigial. During our study, the prolificity rates were examined across three repetitions under the impact of exposure to concentrations of 0.50, 1.00, 2.00, and 4.00 mM of copper (CuSO₄) and lead (Pb(C₂H₃O₂₎₂). Prolificity rates throughout the life cycle exhibited variations as a direct consequence of genetic factors, the concentration of exposure, and the specific type of metal, either copper or lead. The mutant white-vestigial genotype revealed an IC₅₀ concentration for prolificity inhibition at lower doses of 2.00 mM for copper and 4.00 mM for lead, in contrast to the control genotype (wild-type), which exhibited an inhibition concentration rate >IC₅₀ of 4.00 mM only in the case of copper. Our results concluded that the (i) dose influences the prolificity rate in a directly proportional manner, (ii) comparative analyses between copper and lead revealed that copper displayed toxicity across all genotypes within the concentration range of 0.50 mM to 2.00 mM. In contrast, lead exhibited toxicity within the concentration range of 1.00 mM to 4.00 mM, highlighting a (iii) more acute toxicity characteristic in the case of copper. Thus, the results of this research reflect the importance of using Drosophila melanogaster as a genetic model in the comparative study of the interaction between genetic factors and the toxicity of metals, offering significant insights into monitoring their impact and defining the maximum permissible doses on organisms.

Background/Objectives: Population aging is rapidly increasing, and the importance of preventive medicine has been stressed. Health checkup, diet, and exercise are of paramount importance. This study aimed to evaluate the effectiveness of a personalized dual-task intervention that combined exercise with cognitive tasks in improving physical and cognitive functions among independently living elderly individuals. Methods: Participants were aged &gt;65 years who were mostly independent in their activities of daily living, divided into 2 groups. The group receiving the 20-min robot-assisted session was compared to the group receiving traditional functional restoration training. This randomized trial assessed the impact of this intervention on the 30-s chair stand test score and Montreal Cognitive Assessment-Japanese version score of the participants. Results: Both scores significantly improved in the intervention group, indicating enhanced lower-limb function and cognitive capabilities. Conclusions: These findings suggest that integrating cognitive tasks with physical exercise can be a potent strategy to improve overall well-being in the elderly, offering valuable insights for designing comprehensive preventive health programs tailored to this demographic.

Neurological involvement following COronaVIrus Disease 19 (COVID-19) is thought to have a neuroinflammatory etiology. Co-ultraPEALut (an antiinflammatory molecule) and luteolin (an antioxidant) yielded promising results as neuroinflammation antagonists. This study aimed at describing cognitive impairment in post-COVID patient treated with co-ultraPEALut. Montreal Cognitive Assessment (MoCA), Prospective-Retrospective Memory Questionnaire (PRMQ), Fatigue Severity Scale (FSS) and a subjective evaluation were administered at baseline and after 10 months. Co-ultraPEALut-treated patients were retrospectively compared with controls. 26 co-ultraPEALut-treated patients showed significant improvement in PRMQ (T0: 51.94±10.55, T1: 39.67±13.02, p

This article summarizes the results of a research conducted to create an intelligent platform supporting small farmers growing vegetables. In Bulgaria, many farmers use relatively small areas for their production. Considering this situation, the platform is designed for regional use, trying to make maximum use of the specific climatic features of the monitored microdistrict. Consequently, special attention is paid to the possibilities of adapting the platform to the specific environment in which the observed plants grow. Thus, the platform is implemented as an agent-oriented system providing needed flexibility. The core of the platform is a personal assistant, which is responsible for tracking the vegetation cycle of the monitored crop and, upon detection of anomalies, preparing warnings to the farmer. The general architecture of the platform is also presented. In a distributed knowledge base, agriculture-specific knowledge are stored. Furthermore, a standardized interface for receiving data from the sensor networks located in the production areas is briefly described. Currently, the platform is being tested for monitoring four varieties of tomatoes grown on the territory of the national research center for potato breeding. Special attention is paid to the measurements carried out. Conclusions have been drawn that will help improve the platform.

In this paper, we introduce a specialized, fully automated experimental arrangement designed for assessing the effectiveness of augmented and virtual reality technologies in healthcare setting and explore its design principles focusing on depth sensor characterization. The main goal of this project is to establish a robust benchtop platform for the quantitative evaluation of extended reality technologies in a controlled environment, emphasizing the practical assessment of augmented reality head-mounted devices. We outline a design concept and considerations for an experimental configuration aimed at creating a realistic testing environment. This apparatus comprises an observation platform mounted on a three-degree-of-freedom motorized system and a test phantom stage. Focusing on real-case scenarios, we utilized a range of sensors, including readily available range-sensing cameras and commercial augmented reality headsets, such as the Intel RealSense L515 LiDAR camera, mounted on the motion control system. The setup enabled automated data collection and facilitates controlled assessments and validation of various facets of augmented reality technologies, including object, hand, and head tracking, as well as static and dynamic image registration. This paper describes the system architecture, the process of automated data collection, and presents several evaluation studies conducted with this setup, providing insights into the quality and temporal noise associated with static or moving objects or observers in augmented reality applications.

In the whole world, including Mexico, the rising prevalence of diabetes poses significant challenges to healthcare systems, with a notable impact on hospital admissions even being an ambulatory care sensitive condition, that means, hospital admissions are avoidable. Traditional healthcare methodologies have been instrumental in managing diabetes and preventing complications, yet they often face limitations such as merging, cleansing, and outlier analysis of health data to identify and address diseases effectively. This paper aims to address this gap by conducting a comprehensive comparison of the methodologies used in healthcare and data science for this purpose. This work uses hospital diabetes discharge records from 2010 to 2023, a total of 36,665,793 records, which belong to medical units of the Ministry of Health of Mexico. In this work, we seek to provide the arguments why as a data scientist it is mandatory to learn the field of knowledge of the problem and its implications if this is not done, and therefore to disclose insights that can help in policy decisions and reduce the burden of avoidable hospitalizations. The approach is mainly based on the standardization or adjust rates by sex and age groups. This study provides the foundations for a new way of data scientist must deal with health data.

The prolonged static sitting at the workplace is considered as one of the main risks for development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar hyperlordosis and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. This publication proposes the low back pain assessment (LBPA) dataset collected through experiment with 100 participants and consisting of photogrammetric images with intentionally placed body markers, calculated postural angles and tags correct and incorrect posture assessed by habilitated rehabilitator, as well as questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed for recognizing incorrect body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms.

Abstract: Physicians, physical therapists, and occupational therapists have traditionally assessed hand motor function in hemiplegic patients but often struggle to evaluate complex hand move-ments. To address this issue, in 2019, we developed Fahrenheit, a device and algorithm that uses infrared camera image processing to estimate hand paralysis. However, due to Fahrenheit's de-pendency on specialized equipment, we conceived a simpler solution: developing a smartphone app that integrates MediaPipe. The objective of this study was to measure hand movements in stroke patients using both MediaPipe and Fahrenheit and to assess their criterion-related validity. The analysis revealed moderate-to-high correlations between the two methods. Consistent results were also observed in the peak angle and velocity comparisons across severity stages. Because Fahrenheit determines finger recovery status based on these measures, it has the potential to transfer this function to MediaPipe. This study highlighted the potential use of MediaPipe in pa-ralysis estimation applications.

Cancer, characterized by abnormal cell proliferation driven by genetic mutations, remains a significant health challenge. Tailoring effective therapies depends on factors like tumor location, grade, and stage, alongside patient condition. Leveraging Nano-carrier encapsulated drugs offers a promising avenue, as they accumulate preferentially at tumor sites, enhancing therapeutic efficacy while mitigating side effects. Polymeric Nano-carriers, with their customizable properties, further optimize drug delivery efficiency and specificity. Incorporating active targeting ligands such as antibodies, carbohydrates, peptides, folates, and aptamers-onto polymeric nanoparticle surfaces through techniques like functionalization, adsorption, and conjugation plays a pivotal role in directing therapeutic agents to tumor cells. Consequently, polymeric nanoparticles emerge as the carrier of choice for delivering drugs to various tumor types, including breast, colon, lung, prostate, liver, and spleen tumors.

Enhancement of orthodontic tooth movement (OTM) through local administration of biofunctional molecules has become increasingly significant, particularly for adult patients seeking aesthetic and functional improvements. This comprehensive systematic review analyzes the efficacy of various biofunctional molecules in modulating OTM, focusing on the method of administration and its feasibility, especially considering the potential for topical application. A search across multiple databases yielded 36 original articles of experimental human and animal OTM models, which examined biofunctional molecules capable of interfering with the biochemical reactions that cause tooth movement during orthodontic therapy, accelerating the OTM rate through their influence on the bone metabolism (Calcitriol, Prostaglandins, Recombinant human Relaxin, RANKL and RANKL expression plasmid, growth factors, PTH, osteocalcin, vitamin C and E, biocompatible reduced graphene oxide, exogenous thyroxine, sclerostin protein, a specific EP4 agonist (ONO-AE1-329), carrageenan and herbal extracts). The results indicated a variable efficacy in accelerating OTM, with Calcitriol, Prostaglandins (PGE1 and PGE2), RANKL, growth factors and PTH, among others, showing promising outcomes. PGE1, PGE2 and Calcitriol experiments had statistically significant outcomes in both human and animal studies, and, while other molecules underwent only animal testing, they could be validated in the future for human use. Notably, only one of the animal studies explored topical administration, which also suggests a future research direction. This review concluded that while certain biofunctional molecules demonstrated potential for OTM enhancement, the evidence is not definitive. The development of suitable topical formulations for human use could offer a patient-friendly alternative to injections, emphasizing comfort and cost-effectiveness. Future research should focus on overcoming current methodological limitations and advancing translational research to confirm these biomolecules' efficacy and safety in clinical orthodontic practice.

A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral feature and color feature at the same time, and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu ’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu ’er tea were obtained by this system and used for pixel classification. The experimental results show that the accuracy of Support Vector Machine (SVM) model based on combined features is 93%, which is 7% higher than that based on only spectral features. By applying median filtering algorithm and contour detection algorithm to the label matrix extracted from pixel-classified images, 8 impurities except hair were detected successfully. Moreover, taking advantage of the high resolution of visible light camera, small impurities can be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it is proved that the system can obtain multiple images to allow a more detailed and comprehensive understanding of the detected items, and has excellent ability to detect small impurities.