Physical Input Research Articles

Effects of Physical Exercise Input on the Exercise Adherence of College Students: The Chain Mediating Role of Sports Emotional Intelligence and Exercise Self-Efficacy.

The aims of this study were to investigate the effects and mechanisms of physical exercise input, sports emotional intelligence, and sports self-efficacy on exercise adherence, and to examine the chain-mediating role of sports emotional intelligence→sports self-efficacy. The Physical Exercise Input Scale, Exercise Adherence Scale, Sports Emotional Intelligence Scale, and Sports Self-Efficacy Scale were used to investigate 1390 college students in three universities in the Henan Province. (1) Physical exercise input was a significant positive predictor of exercise adherence (β = 0.29, t = 5.78, p < 0.001); (2) sports emotional intelligence and sports self-efficacy mediated the relationship between physical exercise input and exercise adherence; (3) physical exercise input influenced exercise adherence through the separate mediating role of sports emotional intelligence (β = 0.10, t = 5.98, p < 0.001), the separate mediating role of sports self-efficacy (β = 0.13, t = 2.64, p < 0.01), and the chain mediating role of sports emotional intelligence→sports self-efficacy (β = 0.09, t = 2.80, p < 0.01). (1) Physical exercise input can positively predict the level of sports emotional intelligence and sports self-efficacy of college students; (2) Physical exercise input can not only directly influence college students' exercise adherence but can also indirectly influence it through sports emotional intelligence or sports self-efficacy levels alone, as well as through the chain mediation of the two.

Triphenylamine-Based Covalent Organic Framework Films for Dopamine-Responsive Electrofluorochromism.

Covalent organic framework (COF) film with electrofluorochromic (EFC) and electrochromic (EC) properties has been synthesized by using triphenylamine-based monomers. The film exhibited a high maximum fluorescence contrast of 151 when subjected to a drive voltage of 0.75 V vs the Ag/AgCl electrode, causing the fluorescence to be quenched, which resulted in the EFC process's "fluorescence off" state. The switching times for the fluorescence on and off states were 0.51 and 7.79 s, respectively. Over the same voltage range, the COF film also displayed EC properties, achieving a contrast of 50.23% and a coloration efficiency of 297.4 cm2 C-1 at 532 nm, with switching times of 18.6 s for coloration and 0.7 s for bleaching. Notably, the quenched fluorescence of the COF film could be restored by adding dopamine as a reductant. This phenomenon enabled the implementation of a NAND logic gate using the applied potential as a physical input and dopamine addition as a chemical input. This study demonstrates the successful development of COF films with bifunctional EFC and EC properties, showcasing their potential for use in constructing advanced optoelectronic devices.

An Implementation of Communication, Computing and Control Tasks for Neuromorphic Robotics on Conventional Low-Power CPU Hardware

Bioinspired approaches tend to mimic some biological functions for the purpose of creating more efficient and robust systems. These can be implemented in both software and hardware designs. A neuromorphic software part can include, for example, Spiking Neural Networks (SNNs) or event-based representations. Regarding the hardware part, we can find different sensory systems, such as Dynamic Vision Sensors, touch sensors, and actuators, which are linked together through specific interface boards. To run real-time SNN models, specialised hardware such as SpiNNaker, Loihi, and TrueNorth have been implemented. However, neuromorphic computing is still in development, and neuromorphic platforms are still not easily accessible to researchers. In addition, for Neuromorphic Robotics, we often need specially designed and fabricated PCBs for communication with peripheral components and sensors. Therefore, we developed an all-in-one neuromorphic system that emulates neuromorphic computing by running a Virtual Machine on a conventional low-power CPU. The Virtual Machine includes Python and Brian2 simulation packages, which allow the running of SNNs, emulating neuromorphic hardware. An additional, significant advantage of using conventional hardware such as Raspberry Pi in comparison to purpose-built neuromorphic hardware is that we can utilise the built-in physical input–output (GPIO) and USB ports to directly communicate with sensors. As a proof of concept platform, a robotic goalkeeper has been implemented, using a Raspberry Pi 5 board and SNN model in Brian2. All the sensors, namely DVS128, with an infrared module as the touch sensor and Futaba S9257 as the actuator, were linked to a Raspberry Pi 5 board. We show that it is possible to simulate SNNs on a conventional low-power CPU running real-time tasks for low-latency and low-power robotic applications. Furthermore, the system excels in the goalkeeper task, achieving an overall accuracy of 84% across various environmental conditions while maintaining a maximum power consumption of 20 W. Additionally, it reaches 88% accuracy in the online controlled setup and 80% in the offline setup, marking an improvement over previous results. This work demonstrates that the combination of a conventional low-power CPU running a Virtual Machine with only selected software is a viable competitor to neuromorphic computing hardware for robotic applications.

A scaling law approach to rate fabrication tolerances of double-sided electrostatic actuators

Symmetric double-sided electrostatic actuators in push-pull configuration are particularly suitable for linear actuation with low harmonic distortion. However, their motion still is largely determined by pull-in instabilities that are sensitive to geometry variations. A considerable simulation effort is therefore required when assessing manufacturing tolerances during the design process or determining the optimal operating point. Recently, an accurate method was demonstrated, allowing for the numerically inexpensive and experimentally non-destructive extraction of the full quasi-static performance of a clamped-free beam-like electrostatic micro-mechanical actuator with complex 3D design. The key step was to determine the voltage scaling related to the pull-in voltage based on data collected far away from pull-in conditions. This relates a dimensionless ansatz to the physical input voltages as well as the output like e.g. the actuator’s tip deflection. For the chosen approach, however, the relationship between the model and the geometry parameters is unknown. In this paper we propose a method to enable quantifying the impact of geometry parameter variations. In particular, we adapt the model equation for the case of symmetry-breaking tolerances on the basis of few FEM-simulations. The quasi-static pull-in instability, as well as the nonlinear deflection, are consistently reproduced over the full range of relevant combinations of signal and bias voltages. Our analysis was developed in the context of a specific electro-acoustic transducer. However, we find indications that the underlying method is in fact applicable to a much broader range of micro-mechanical actuators.

A Benchmarking Method to Rank the Performance of Physics-Based Earthquake Simulations

Abstract Physics-based earthquake simulators are an increasingly popular modeling tool in earthquake forecasting for seismic hazard as well as fault rupture behavior studies. Their popularity comes from their ability to overcome completeness limitations of real catalogs, and also because they allow reproducing complex fault rupture and interaction patterns via modeling the physical processes involved in earthquake nucleation and propagation. One important challenge of these models revolves around selecting the physical input parameters that will yield the better similarity to earthquake relationships observed in nature, for instance, the frictional parameters of the rate-and-state law—a and b—or the initial normal and shear stresses. Because of the scarcity of empirical data, such input parameters are often selected by trial–error exploration and predominantly manual model performance analyses, which can overall be time consuming. We present a new benchmarking approach to analyze and rank the relative performance of simultaneous earthquake simulation catalogs by quantitatively scoring their combined fit to three reference function types: (1) earthquake-scaling relationships, (2) the shape of the magnitude–frequency distributions, and (3) the rates of the surface ruptures from paleoseismology or paleoearthquake occurrences. The approach provides an effective and potentially more efficient approximation to easily identify the models and input parameter combinations that fit more closely to earthquake relations and behavior. The approach also facilitates the exhaustive analysis of many input parameter combinations, identifying systematic correlations between parameters and model outputs that can potentially improve the overall understanding of the physics-based models. Finally, we demonstrate how the method results agree with the published findings in other earthquake simulation evaluations, a fact that reinforces its overall usefulness. The model ranking outputs can be useful for subsequent analyses, particularly in seismic hazard applications, such as the selection of appropriate earthquake occurrence rate models and their weighting for a logic tree.

Levelized Cost and Carbon Intensity of Solar Hydrogen Production from Water Electrolysis Using a Scalable and Intrinsically Safe Photocatalytic Z-Scheme Raceway System

The realization of an environmentally sustainable and widely-adopted hydrogen economy may require lowering hydrogen production costs of production pathways with ultra-low greenhouse gas emissions to $1/kg H2. The allocation of new or existing renewable electricity generation solely to hydrogen production remains contentious due to disputes regarding emissions accounting. Photoelectrochemical (PEC) hydrogen production technologies offer a unique solution, as hydrogen is produced directly from solar energy and water, without the need for electricity generation. However, cost projections for past photoelectrochemical designs have suggested that they are not cost competitive compared to conventional electrolysis systems manufactured at scale. Herein, we offer the first illustrative benchmark of cost and carbon intensity of hydrogen produced in a Type 2 Z-scheme photocatalytic reactor design that employs suspended semiconducting nanoparticles organized in two stacked baggies in a raceway design.To explore the near-term and future cost projection for hydrogen production via Type 2 photocatalytic Z-scheme raceways, the authors developed a bottom-up total installed capital cost model. This project cost model incorporates: 1) raceway reactor cost, derived from a Design for Manufacturing and Assembly (DFMA) process-based cost model, 2) mechanical balance of plant, including process equipment, piping, valves, and instrumentation, derived from equipment quotes, scaling, database values, and Aspen estimates; 3) electrical balance of plant, including wiring, derived from time and material cost correlations; 4) Site Preparation, focused on green field installation; and 5) Construction Overhead, including engineering, procurement, and construction (EPC) costs and project contingency. The capital cost model is fed into a Levelized Cost of Hydrogen (LCOH) discounted cash flow model that accounts for electricity and water consumption, in addition to other operating costs, including labor, maintenance, and raceway replacements.A hybrid life-cycle approach was used to develop an inventory of greenhouse gas emissions from all materials and energy flows associated with the facility life cycle. These flows are converted to emissions based on specific emission factors, or by using physical units-based input–output LCA models associated with the broader economic impacts of fuel and materials production, use, and end use.Estimates for levelized cost of hydrogen suggest that a 50 metric ton H2 per day (MTD) raceway plant can approach $2.50/kg H2 at an 8% solar-to-hydrogen efficiency with further cost improvements possible through increased performance and larger plant scales. Carbon intensity is estimated to be well under clean hydrogen targets for global warming potential of <2-4 kg CO2e/kg H2 produced. The results suggest a highly competitive and scalable technology, that justifies further experimental validation and prototyping in the field. Figure 1

Integrating electromagnetic cancer stress with immunotherapy: a therapeutic paradigm.

An array of published cell-based and small animal studies have demonstrated a variety of exposures of cancer cells or experimental carcinomas to electromagnetic (EM) wave platforms that are non-ionizing and non-thermal. Overall effects appear to be inhibitory, inducing cancer cell stress or death as well as inhibition in tumor growth in experimental models. A variety of physical input variables, including discrete frequencies, amplitudes, and exposure times, have been tested, but drawing methodologic rationale and mechanistic conclusions across studies is challenging. Nevertheless, outputs such as tumor cytotoxicity, apoptosis, tumor membrane electroporation and leak, and reactive oxygen species generation are intriguing. Early EM platforms in humans employ pulsed electric fields applied either externally or using interventional tumor contact to induce tumor cell electroporation with stromal, vascular, and immunologic sparing. It is also possible that direct or external exposures to non-thermal EM waves or pulsed magnetic fields may generate electromotive forces to engage with unique tumor cell properties, including tumor glycocalyx to induce carcinoma membrane disruption and stress, providing novel avenues to augment tumor antigen release, cross-presentation by tumor-resident immune cells, and anti-tumor immunity. Integration with existing checkpoint inhibitor strategies to boost immunotherapeutic effects in carcinomas may also emerge as a broadly effective strategy, but little has been considered or tested in this area. Unlike the use of chemo/radiation and/or targeted therapies in cancer, EM platforms may allow for the survival of tumor-associated immunologic cells, including naïve and sensitized anti-tumor T cells. Moreover, EM-induced cancer cell stress and apoptosis may potentiate endogenous tumor antigen-specific anti-tumor immunity. Clinical studies examining a few of these combined EM-platform approaches are in their infancy, and a greater thrust in research (including basic, clinical, and translational work) in understanding how EM platforms may integrate with immunotherapy will be critical in driving advances in cancer outcomes under this promising combination.

End-to-end simulation of particle physics events with flow matching and generator oversampling

The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be used to replicate accurate simulations from traditional approaches with several orders of magnitude of speed-up. The classical simulation chain starts from a physics process of interest, computes energy deposits of particles and electronics response, and finally employs the same reconstruction algorithms used for data. Eventually, the data are reduced to some high-level analysis format. Instead, we propose an end-to-end approach, simulating the final data format directly from physical generator inputs, skipping any intermediate steps. We use particle jets simulation as a benchmark for comparing both discrete and continuous Normalizing Flows models. The models are validated across a variety of metrics to identify the most accurate. We discuss the scaling of performance with the increase in training data, as well as the generalization power of these models on physical processes different from the training one. We investigate sampling multiple times from the same physical generator inputs, a procedure we name oversampling, and we show that it can effectively reduce the statistical uncertainties of a dataset. This class of ML algorithms is found to be capable of learning the expected detector response independently of the physical input process. The speed and accuracy of the models, coupled with the stability of the training procedure, make them a compelling tool for the needs of current and future experiments.

Efficient estimation procedure for failure probability function by an augmented directional sampling

AbstractFailure probability function (FPF) can reflect quantitative effects of random input distribution parameter (DP) on failure probability, and it is significant for decoupling reliability‐based design optimization (RBDO). But the FPF estimation is time‐consuming since it generally requires repeated reliability analyses at different DPs. For efficiently estimating FPF, an augmented directional sampling (A‐DS) is proposed in this paper. By using the property that the limit state surface (LSS) in physical input space is independent of DP, the A‐DS establishes transformation of LSS samples in standard normal spaces corresponding to different DPs. By the established transformation in different standard normal spaces, the LSS samples obtained by DS at a given DP can be transformed to those at other DPs. After simple interpolation post‐processing on those transformed samples, the failure probability at other DPs can be estimated by DS simultaneously. The main novelty of A‐DS is that a strategy of sharing DS samples is designed for estimating the failure probability at different DPs. The A‐DS avoids repeated reliability analyses and inherits merit of DS suitable for solving problems with multiple failure modes and small failure probability. Compared with other FPF estimation methods, the examples sufficiently verify the accuracy and efficiency of A‐DS.

Application of factorization machine with quantum annealing to hyperparameter optimization and metamodel‐based optimization in granular flow simulations

AbstractThis study examined the applicability of factorization machines with quantum annealing (FMQA) to the field of landslide risk assessment for two specific black‐box optimization problems, hyperparameter optimization (HPO) for metamodeling and metamodel‐based simulation optimization (MBSO) targeting granular flow simulation using discrete element method (DEM). These two optimization problems are solved successively: HPO is first performed to determine the hyperparameters of the Gaussian process regression (GPR) metamodel, which is then used as a low‐cost, fast approximate solver of granular flow simulations for MBSO. After conducting a series of granular flow simulations using DEM, a metamodel is created that outputs a risk index of interest, the run‐out distance, from its input parameters by employing GPR with two hyperparameters, length‐scale and signal variance. Subsequently, HPO is performed to obtain the optimal set of hyperparameters by applying FMQA and other optimization methods using another set of hyperparameters determined using the gradient‐ascent method as the reference solution. Finally, using the metamodel created by each optimization method as an approximate solver for DEM simulations, MBSO is performed to find the optimal target output, the maximum run‐out distance, in the space of physical input parameters for risk assessment. A comparison of the performance of FMQA with that of other methods shows that FMQA is competitive in terms of efficiency and stability with state‐of‐the‐art algorithms such as Bayesian optimization.

Spread complexity in saddle-dominated scrambling

Recently, the concept of spread complexity, Krylov complexity for states, has been introduced as a measure of the complexity and chaoticity of quantum systems. In this paper, we study the spread complexity of the thermofield double state within integrable systems that exhibit saddle-dominated scrambling. Specifically, we focus on the Lipkin-Meshkov-Glick model and the inverted harmonic oscillator as representative examples of quantum mechanical systems featuring saddle-dominated scrambling. Applying the Lanczos algorithm, our numerical investigation reveals that the spread complexity in these systems exhibits features reminiscent of chaotic systems, displaying a distinctive ramp-peak-slope-plateau pattern. Our results indicate that, although spread complexity serves as a valuable probe, accurately diagnosing true quantum chaos generally necessitates additional physical input. We also explore the relationship between spread complexity, the spectral form factor, and the transition probability within the Krylov space. We provide analytical confirmation of our numerical results, validating the Ehrenfest theorem of complexity and identifying a distinct quadratic behavior in the early-time regime of spread complexity.

Virtual Mouse

Now a days the use of hand gesture recognition became popular due to the advance technology of artificial Intelligence (AI). In recent years, there has been a growing interest in developing alternative methods for human-computer interaction (HCI) that go beyond traditional input devices like keyboards and mouse. Hand gesture recognition has emerged as a promising approach, offering intuitive and natural ways for users to interact with digital interfaces. The proposed system utilizes computer vision techniques to detect and track hand gestures in real-time. By mapping specific gestures to mouse control commands, users can navigate and interact with graphical user interfaces (GUIs) without the need for physical input devices. In this paper A hand gesture- controlled virtual mouse system utilizes the AI and Machine learning algorithms to recognize the proper hand gestures and translate them into the mouse movements. The system we are designed that the people who have problems or difficulty using a traditional mouse or keyboard it will be appropriate for them. For this we are using camera that capture images of the user’s hand, which are processed by an AI and ML algorithm to recognize the gesture of hands being made. We trained the system by using a dataset of hand gestures. Keywords — Hand gesture recognition, Machine Learning, Artificial Intelligence, Virtual Mouse, Python, Media Pipe

Augmented Virtual Mouse System with Enhanced Gesture Recognition

This paper presents the development of a contactless system designed to serve as an alternative to the traditional physical input device, such as a computer mouse or touchpad, utilized in human-computer interaction. The proposed system aims to offer a more convenient and hygienic way of interacting with computer devices by using hand gestures instead of physical contact with the mouse system or machine interface. The system has potential applications in various fields including healthcare, public interfaces, and gaming. Existing gesture-controlled input systems lack a comprehensive touchless and accurate system capable of performing almost every operation of a physical mouse. The proposed approach involves recognising the hand and its gestures. Hand tracking and dynamic gesture recognition are incorporated to ensure a smooth and hassle-free user experience during interaction. The efficient Mediapipe framework is used to detect and track hand movements from video frames captured through a computing device Camera.Computer vision algorithms are employed for gesture recognition within each frame, along with Python libraries for processing gestures. Sufficient gestures are added into the system, enabling it to execute a wide range of mouse functions with substantial precision metrics. This paper provides details regarding the development and overall performance of the system. Key Words: hands, mouse, contactless, gesture, OpenCV, mediapipe

Credit creation, credit destruction and credit reallocation: Firm-level evidence from India

The rapidly growing empirical studies find that reallocating physical and financial input across and within firms can be an essential productivity growth source. This paper examines the process of credit reallocation across Indian businesses by applying the methodology proposed by Davis & Haltiwanger (1992) for measuring job reallocation. Using the unique firm-level dataset for over 30 years, we find that substantial gross credit flows are masked by underlying net credit growth at any business cycle phase. Our results reveal that credit reallocation is intense, and the majority of credit reallocation occurs within a group of firms similar in size, governance, or industry. We also find that credit destruction is more volatile than credit creation, and excess reallocation fluctuates countercyclically over the business cycle. The findings suggest that heterogeneity in credit market dynamics is a prime source of credit reallocation evolution.

Pattern Recognition in Embedded Systems for Event Occurrences

Abstract: PR (pattern recognition) typically includes interaction with humans and other complicated processes in the real world, embedded systems are ideal candidates. A typical PR application often considered the more perceptual branch of AI, responds to external events that the system detects through physical sensors or input devices by activating actuators or displaying relevant information. To explore the embedded recognition system and apply the deep learning algorithm to face detection, the deep learning-based Convolutional Neural Network (CNN)suggests two deep face detection methods. These are presented to use the deep learning algorithm.This was done to make it possible for us to use the deep learning algorithm for face detection. Because of this, to analyze the built-in face recognition system and applied the deep learning algorithm to the process of identifying faces. In addition, to do both things simultaneously. OMTCNN's training accuracy is 85.14%, higher than the unimproved algorithm. Accuracy of the recognition and calculation acceleration modules boosts embedded system face detection and identification performance. Embedded deep learning recognition is helpful.

Quasi-Global Sensitivity Analysis of Supersonic Computational Fluid Dynamics Simulations

Determining the sensitivity of model outputs to input parameters is an important precursor to developing informative parameter studies, building surrogate models, and performing rigorous uncertainty quantification. Determining parameter sensitivities over a range of parameter values, termed global sensitivity analysis, requires many model evaluations sampled over the parameter space, which is intractable for many large-scale computational fluid dynamics (CFD) applications. For moderate parameter dimensions, we propose the use of Morris screening, a one-at-a-time quasi-global method for estimating parameter sensitivities over a range of parameter values for CFD simulations. The Morris method is implemented within a CFD framework that utilizes adaptive grid refinement, thereby enabling its application to state-of-the-art production-level problems. The method is shown to be viable for a model problem of supersonic flow over an axisymmetric capsule geometry with both physical and nonphysical (i.e., simulation-informing) input parameters. It is shown that Morris screening identifies relative parameter sensitivities consistent with those of more costly experimental and computational studies that were previously performed on this geometry.

Integrating microdosimetricin vitroRBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeliNg for RBE ASsessment (MONAS) tool.

Objective.In this paper, we present MONAS (MicrOdosimetry-based modelliNg for relative biological effectiveness (RBE) ASsessment) toolkit. MONAS is a TOPAS Monte Carlo extension, that combines simulations of microdosimetric distributions with radiobiological microdosimetry-based models for predicting cell survival curves and dose-dependent RBE.Approach.MONAS expands TOPAS microdosimetric extension, by including novel specific energy scorers to calculate the single- and multi-event specific energy microdosimetric distributions at different micrometer scales. These spectra are used as physical input to three different formulations of themicrodosimetric kinetic model, and to thegeneralized stochastic microdosimetric model(GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra andin vitrosurvival fraction data. To show the MONAS features, we present two different applications of the code: (i) the depth-RBE curve calculation from a passively scattered proton SOBP and monoenergetic12C-ion beam by using experimentally validated spectra as physical input, and (ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons.Main results.MONAS can estimate dose-dependent RBE and cell survival curves from experimentally validated microdosimetric spectra with four clinically relevant radiobiological models. From the radiobiological characterization of a proton SOBP and12C fields, we observe the well-known trend of increasing RBE values at the distal edge of the radiation field. The 3D RBE map calculated confirmed the trend observed in the analysis of the SOBP, with the highest RBE values found in the distal edge of the target.Significance.MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, pushing closer the experimental physics-based description to the biological damage assessment, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE.

1958 Promoting Bone Health by ensuring in-patient Ortho-geriatrician Bone Health plan in notes following Neck of Femur Fracture

Abstract Introduction and Aims Osteoporosis affects 3 million people in the UK with more than 500,000 hospital presentations annually due to fragility fractures costing in excess of £4.4 billion to the NHS. Bone protective medications are a cost-effective way of reducing fracture and admission following a fall. The Royal College of Physicians National Hip Fracture Database targets that patients are: “given suitable bone strengthening treatment and followed up to ensure that they are still receiving this protection 120 days after fracture”. Lewisham Hospital achieves this in only 22% of suitable patients against a national average of 35%. This project aimed to increase the number of eligible NOF patients on bone protective medication. Method Data was collected for patients over the age of 65 admitted with NOF. 22 eligible patients were admitted from 01/01/23 to 28/02/23 and 16 from 01/03/23 to 31/05/23. It was identified whether an appropriate bone health plan, including FRAX and calcium/vitamin D supplementation, was recorded in the medical notes and electronic departure note (EDN). Interventions included an advice sheet for rotating doctors, additional education at induction and a bone health proforma for medical notes and EDNs. Results 86% of patients in cohort one had bone health plans in their notes and 59% in their EDN. 64% commenced bisphosphonates with 1 eligible patient (4.5%) not receiving medication. After the interventions 100% patients had a bone health plan in their notes and 80% on their EDN. 46.7% of this cohort commenced on bisphosphonates which equated to 100% of patients appropriate for bone protective medication. Conclusion(s) Providing guidance and education to rotating doctors to ensure Geriatrician-led bone health planning resulted in all eligible patients commencing bone protective medication and total numbers above the national average.

New estimate of shadow economy based on the total energy consumption. Evidence from the European Union countries

Estimating the shadow economy has become a significant concern for politicians, economists, and social scientists in recent times. Despite various advancements in methodology and empirical shreds of evidence, some areas remain unexplored. This article provides a valuable database on the estimations of the shadow economy in 26 EU countries for the period 2001–2021. We develop an innovative technique for measuring the shadow economy that is distinct from previous methods in the literature since it relies upon a modified version of the physical input approach. Instead of relying solely on electric power consumption, we consider the total final energy consumption, which encompasses all forms of energy utilized in the economy. In addition, advanced econometric modeling is performed through robust fixed effects LSDV model to delve into the credentials of our method. The main findings reveal significant variation in average levels of the shadow economy in the European Union, which range between 9.5% and 40% of the official GDP over the analyzed period. The developed database is intended to serve as a valuable resource for policymakers as they strive to combat economic crime. Its widespread utilization is anticipated, as a tool that will play an integral role in informing the development of effective policies and strategies in this critical area.

2009 Integrated physical-mental health care models for older people under specialist mental health services: a systematic review

Abstract Background Older people have complex health needs, with the inter-play between physical and mental health being a prominent issue. The ageing population has resulted in a large proportion of older people living with co-occurring physical and mental health disorders, which can prove challenging to manage simultaneously, particularly for serious mental illness. The aim of this systematic review was to explore models of integrated physical-mental health care available for older people, and whether these result in improved health outcomes. Sources of heterogeneity in the current evidence base alongside limitations were also explored. Methods Medline, Embase, CINAHL, PsycINFO and Scopus were searched with a predefined search strategy, generating 5257 articles. Studies were suitable for inclusion where an integrated physical-mental health care service model was utilised in a population of older people (aged &amp;gt;60 years) with a mental health diagnosis and at least one concomitant physical health condition requiring physical health care input. All studies were quality assessed for risk of bias and results were synthesised narratively. Results Ten studies met the inclusion criteria. All studies incorporated service models involving integrated and/or multidisciplinary care. These included joint medical-mental health wards as well as the implementation of multidisciplinary teams in hospital and care facilities. Overall, this enhanced the quality of care for elderly patients with benefits including but not limited to, enhanced patient experience, the expansion of multidisciplinary team practice, improved management of illness, and timely intervention. Conclusions Multidisciplinary and integrated care resulted in improvement of a range of health outcomes for older people with combined physical and mental health needs. Larger and more robust studies are needed to explore the development of these service models further, with cost-effectiveness analyses.